Автоматический выбор классификационной модели с оптимизированными гиперпараметрами
Данные предиктора и ответа, fitcauto автоматически пытается выбрать типы классификационных моделей с различными значениями гиперпараметров. Функция использует байесовскую оптимизацию для выбора моделей и их значений гиперпараметров и вычисляет ошибку классификации перекрестной проверки для каждой модели. После завершения оптимизации fitcauto возвращает модель, обученную всему набору данных, которая, как ожидается, наилучшим образом классифицирует новые данные. Вы можете использовать predict и loss объектные функции возвращаемой модели для классификации новых данных и вычисления ошибки классификации тестового набора соответственно.
Использовать fitcauto когда вы не уверены, какие типы классификаторов лучше всего подходят для ваших данных. Сведения об альтернативных методах настройки гиперпараметров классификационных моделей см. в разделе Альтернативная функциональность.
Mdl = fitcauto(Tbl,ResponseVarName)Mdl с настроенными гиперпараметрами. Стол Tbl содержит переменные предиктора и переменную ответа, где ResponseVarName - имя переменной ответа.
Mdl = fitcauto(___,Name,Value)HyperparameterOptimizationOptions аргумент пары имя-значение для указания способа выполнения байесовской оптимизации.
[ дополнительно возвращает Mdl,OptimizationResults] = fitcauto(___)OptimizationResults, a BayesianOptimization объект, содержащий результаты процесса выбора модели и настройки гиперпараметров.
Использовать fitcauto для автоматического выбора классификационной модели с оптимизированными гиперпараметрами, заданными данными предиктора и ответа, хранящимися в таблице.
Загрузить данные
Загрузить carbig набор данных, содержащий замеры автомобилей, сделанные в 1970-х и начале 1980-х годов.
load carbigКлассифицировать автомобили исходя из того, были ли они изготовлены в США.
Origin = categorical(cellstr(Origin));
Origin = mergecats(Origin,{'France','Japan','Germany', ...
'Sweden','Italy','England'},'NotUSA');Создание таблицы, содержащей переменные предиктора Acceleration, Displacementи так далее, а также переменная ответа Origin.
cars = table(Acceleration,Displacement,Horsepower, ...
Model_Year,MPG,Weight,Origin);Данные раздела
Разбейте данные на обучающие и тестовые наборы. Используйте приблизительно 80% наблюдений для процесса выбора модели и настройки гиперпараметров, а 20% наблюдений для проверки производительности конечной модели, возвращенной fitcauto. Использовать cvpartition для секционирования данных.
rng('default') % For reproducibility of the data partition c = cvpartition(Origin,'Holdout',0.2); trainingIdx = training(c); % Training set indices carsTrain = cars(trainingIdx,:); testIdx = test(c); % Test set indices carsTest = cars(testIdx,:);
Управляемый fitcauto
Передача данных обучения в fitcauto. По умолчанию fitcauto определяет подходящие типы моделей для попытки, использует байесовскую оптимизацию для поиска хороших значений гиперпараметров и возвращает обученную модель Mdl с наилучшей ожидаемой производительностью. Дополнительно, fitcauto предоставляет график оптимизации и итеративное отображение результатов оптимизации. Дополнительные сведения о том, как интерпретировать эти результаты, см. в разделе Подробный просмотр.
Ожидайте, что этот процесс займет некоторое время. Чтобы ускорить процесс оптимизации, рекомендуется задать параллельное выполнение оптимизации при наличии лицензии Parallel Computing Toolbox™. Для этого необходимо пройти 'HyperparameterOptimizationOptions',struct('UseParallel',true) кому fitcauto в качестве аргумента пары имя-значение.
Mdl = fitcauto(carsTrain,'Origin');Warning: It is recommended that you first standardize all numeric predictors when optimizing the Naive Bayes 'Width' parameter. Ignore this warning if you have done that.
Learner types to explore: ensemble, knn, nb, svm, tree Total iterations (MaxObjectiveEvaluations): 150 Total time (MaxTime): Inf |=================================================================================================================================| | Iter | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | result | loss | & validation (sec)| validation loss | validation loss | | | |=================================================================================================================================| | 1 | Best | 0.14154 | 10.563 | 0.14154 | 0.14154 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 201 | | | | | | | | | MinLeafSize: 7 |
| 2 | Accept | 0.18269 | 0.57392 | 0.14154 | 0.14154 | knn | NumNeighbors: 3 |
| 3 | Accept | 0.23397 | 0.1264 | 0.14154 | 0.14154 | knn | NumNeighbors: 91 |
| 4 | Accept | 0.16308 | 12.867 | 0.14154 | 0.15468 | ensemble | Method: LogitBoost | | | | | | | | | NumLearningCycles: 274 | | | | | | | | | MinLeafSize: 15 |
| 5 | Accept | 0.20833 | 0.124 | 0.14154 | 0.15468 | knn | NumNeighbors: 4 |
| 6 | Accept | 0.22115 | 0.079641 | 0.14154 | 0.15468 | knn | NumNeighbors: 28 |
| 7 | Accept | 0.16923 | 0.20013 | 0.14154 | 0.15468 | tree | MinLeafSize: 105 |
| 8 | Accept | 0.37179 | 0.59222 | 0.14154 | 0.15468 | svm | BoxConstraint: 0.022186 | | | | | | | | | KernelScale: 0.085527 |
| 9 | Accept | 0.37179 | 0.11659 | 0.14154 | 0.15468 | svm | BoxConstraint: 0.045899 | | | | | | | | | KernelScale: 0.0024758 |
| 10 | Accept | 0.24615 | 0.98386 | 0.14154 | 0.15468 | nb | DistributionNames: kernel | | | | | | | | | Width: 1.1327 |
|=================================================================================================================================| | Iter | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | result | loss | & validation (sec)| validation loss | validation loss | | | |=================================================================================================================================| | 11 | Accept | 0.16923 | 0.079996 | 0.14154 | 0.15468 | tree | MinLeafSize: 78 |
| 12 | Accept | 0.26923 | 0.10923 | 0.14154 | 0.15468 | svm | BoxConstraint: 11.063 | | | | | | | | | KernelScale: 15.114 |
| 13 | Best | 0.12923 | 0.11568 | 0.12923 | 0.15468 | tree | MinLeafSize: 3 |
| 14 | Accept | 0.21154 | 0.084406 | 0.12923 | 0.15468 | knn | NumNeighbors: 2 |
| 15 | Accept | 0.14154 | 0.080022 | 0.12923 | 0.15294 | tree | MinLeafSize: 1 |
| 16 | Accept | 0.14769 | 0.092395 | 0.12923 | 0.15097 | tree | MinLeafSize: 2 |
| 17 | Accept | 0.14154 | 10.869 | 0.12923 | 0.14872 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 208 | | | | | | | | | MinLeafSize: 10 |
| 18 | Accept | 0.37179 | 0.12386 | 0.12923 | 0.14872 | svm | BoxConstraint: 116.46 | | | | | | | | | KernelScale: 0.52908 |
| 19 | Accept | 0.22769 | 0.15545 | 0.12923 | 0.14872 | nb | DistributionNames: normal | | | | | | | | | Width: NaN |
| 20 | Accept | 0.22115 | 0.070813 | 0.12923 | 0.14872 | knn | NumNeighbors: 8 |
|=================================================================================================================================| | Iter | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | result | loss | & validation (sec)| validation loss | validation loss | | | |=================================================================================================================================| | 21 | Accept | 0.37179 | 0.11553 | 0.12923 | 0.14872 | svm | BoxConstraint: 45.341 | | | | | | | | | KernelScale: 0.76949 |
| 22 | Accept | 0.12923 | 0.080362 | 0.12923 | 0.14194 | tree | MinLeafSize: 3 |
| 23 | Best | 0.10154 | 0.079656 | 0.10154 | 0.13213 | tree | MinLeafSize: 5 |
| 24 | Accept | 0.22769 | 0.2529 | 0.10154 | 0.13213 | nb | DistributionNames: kernel | | | | | | | | | Width: 0.42571 |
| 25 | Accept | 0.11385 | 0.080085 | 0.10154 | 0.1289 | tree | MinLeafSize: 11 |
| 26 | Accept | 0.13782 | 0.092228 | 0.10154 | 0.1289 | svm | BoxConstraint: 9.7286 | | | | | | | | | KernelScale: 293.41 |
| 27 | Accept | 0.22769 | 0.073346 | 0.10154 | 0.1289 | nb | DistributionNames: normal | | | | | | | | | Width: NaN |
| 28 | Accept | 0.21795 | 0.074914 | 0.10154 | 0.1289 | knn | NumNeighbors: 42 |
| 29 | Accept | 0.24308 | 0.27621 | 0.10154 | 0.1289 | nb | DistributionNames: kernel | | | | | | | | | Width: 4.4662 |
| 30 | Accept | 0.16308 | 12.328 | 0.10154 | 0.1289 | ensemble | Method: LogitBoost | | | | | | | | | NumLearningCycles: 267 | | | | | | | | | MinLeafSize: 131 |
|=================================================================================================================================| | Iter | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | result | loss | & validation (sec)| validation loss | validation loss | | | |=================================================================================================================================| | 31 | Accept | 0.24308 | 0.22334 | 0.10154 | 0.1289 | nb | DistributionNames: kernel | | | | | | | | | Width: 0.66296 |
| 32 | Accept | 0.22115 | 0.066711 | 0.10154 | 0.1289 | knn | NumNeighbors: 28 |
| 33 | Accept | 0.13846 | 0.079934 | 0.10154 | 0.12465 | tree | MinLeafSize: 25 |
| 34 | Accept | 0.21474 | 0.085438 | 0.10154 | 0.12465 | knn | NumNeighbors: 14 |
| 35 | Accept | 0.16615 | 10.05 | 0.10154 | 0.12465 | ensemble | Method: LogitBoost | | | | | | | | | NumLearningCycles: 215 | | | | | | | | | MinLeafSize: 13 |
| 36 | Accept | 0.14154 | 12.866 | 0.10154 | 0.12465 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 254 | | | | | | | | | MinLeafSize: 31 |
| 37 | Accept | 0.22769 | 0.070251 | 0.10154 | 0.12465 | nb | DistributionNames: normal | | | | | | | | | Width: NaN |
| 38 | Accept | 0.37179 | 0.077924 | 0.10154 | 0.12465 | svm | BoxConstraint: 0.0073633 | | | | | | | | | KernelScale: 774.33 |
| 39 | Accept | 0.16923 | 0.068411 | 0.10154 | 0.12552 | tree | MinLeafSize: 82 |
| 40 | Accept | 0.20833 | 0.064563 | 0.10154 | 0.12552 | knn | NumNeighbors: 4 |
|=================================================================================================================================| | Iter | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | result | loss | & validation (sec)| validation loss | validation loss | | | |=================================================================================================================================| | 41 | Accept | 0.16308 | 12.932 | 0.10154 | 0.12552 | ensemble | Method: LogitBoost | | | | | | | | | NumLearningCycles: 274 | | | | | | | | | MinLeafSize: 150 |
| 42 | Accept | 0.22462 | 0.24365 | 0.10154 | 0.12552 | nb | DistributionNames: kernel | | | | | | | | | Width: 121.64 |
| 43 | Accept | 0.20308 | 11.027 | 0.10154 | 0.12552 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 229 | | | | | | | | | MinLeafSize: 117 |
| 44 | Accept | 0.16923 | 0.069279 | 0.10154 | 0.12291 | tree | MinLeafSize: 84 |
| 45 | Accept | 0.22769 | 0.078716 | 0.10154 | 0.12291 | nb | DistributionNames: normal | | | | | | | | | Width: NaN |
| 46 | Accept | 0.22769 | 0.068458 | 0.10154 | 0.12291 | nb | DistributionNames: normal | | | | | | | | | Width: NaN |
| 47 | Accept | 0.16615 | 9.9849 | 0.10154 | 0.12291 | ensemble | Method: LogitBoost | | | | | | | | | NumLearningCycles: 212 | | | | | | | | | MinLeafSize: 49 |
| 48 | Accept | 0.14769 | 14.541 | 0.10154 | 0.12291 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 288 | | | | | | | | | MinLeafSize: 25 |
| 49 | Accept | 0.23077 | 0.21379 | 0.10154 | 0.12291 | nb | DistributionNames: kernel | | | | | | | | | Width: 73.249 |
| 50 | Accept | 0.37179 | 0.091937 | 0.10154 | 0.12291 | svm | BoxConstraint: 0.0036501 | | | | | | | | | KernelScale: 1.0504 |
|=================================================================================================================================| | Iter | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | result | loss | & validation (sec)| validation loss | validation loss | | | |=================================================================================================================================| | 51 | Accept | 0.21474 | 0.098808 | 0.10154 | 0.12291 | svm | BoxConstraint: 64.859 | | | | | | | | | KernelScale: 23.779 |
| 52 | Accept | 0.37179 | 0.10415 | 0.10154 | 0.12291 | svm | BoxConstraint: 0.16622 | | | | | | | | | KernelScale: 4.4901 |
| 53 | Accept | 0.25846 | 0.2444 | 0.10154 | 0.12291 | nb | DistributionNames: kernel | | | | | | | | | Width: 0.079498 |
| 54 | Accept | 0.21154 | 0.074835 | 0.10154 | 0.12291 | knn | NumNeighbors: 2 |
| 55 | Accept | 0.13846 | 12.173 | 0.10154 | 0.12291 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 234 | | | | | | | | | MinLeafSize: 8 |
| 56 | Accept | 0.36538 | 0.10958 | 0.10154 | 0.12291 | svm | BoxConstraint: 271.6 | | | | | | | | | KernelScale: 2.743 |
| 57 | Accept | 0.16615 | 11.482 | 0.10154 | 0.12291 | ensemble | Method: LogitBoost | | | | | | | | | NumLearningCycles: 248 | | | | | | | | | MinLeafSize: 117 |
| 58 | Accept | 0.37179 | 0.095419 | 0.10154 | 0.12291 | svm | BoxConstraint: 7.5785 | | | | | | | | | KernelScale: 0.0066815 |
| 59 | Accept | 0.37179 | 0.097469 | 0.10154 | 0.12291 | svm | BoxConstraint: 0.0017765 | | | | | | | | | KernelScale: 0.86786 |
| 60 | Accept | 0.37179 | 0.11284 | 0.10154 | 0.12291 | svm | BoxConstraint: 0.011465 | | | | | | | | | KernelScale: 0.02747 |
|=================================================================================================================================| | Iter | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | result | loss | & validation (sec)| validation loss | validation loss | | | |=================================================================================================================================| | 61 | Accept | 0.11692 | 0.077515 | 0.10154 | 0.12239 | tree | MinLeafSize: 12 |
| 62 | Accept | 0.29167 | 0.091617 | 0.10154 | 0.12239 | svm | BoxConstraint: 11.939 | | | | | | | | | KernelScale: 11.002 |
| 63 | Accept | 0.21795 | 0.067171 | 0.10154 | 0.12239 | knn | NumNeighbors: 6 |
| 64 | Accept | 0.18269 | 0.062887 | 0.10154 | 0.12239 | knn | NumNeighbors: 3 |
| 65 | Accept | 0.12923 | 0.075704 | 0.10154 | 0.11989 | tree | MinLeafSize: 3 |
| 66 | Accept | 0.16923 | 0.065889 | 0.10154 | 0.12048 | tree | MinLeafSize: 56 |
| 67 | Accept | 0.1891 | 0.068215 | 0.10154 | 0.12048 | knn | NumNeighbors: 1 |
| 68 | Accept | 0.13231 | 14.135 | 0.10154 | 0.12048 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 270 | | | | | | | | | MinLeafSize: 4 |
| 69 | Accept | 0.22769 | 0.060902 | 0.10154 | 0.12048 | nb | DistributionNames: normal | | | | | | | | | Width: NaN |
| 70 | Accept | 0.37231 | 0.24511 | 0.10154 | 0.12048 | nb | DistributionNames: kernel | | | | | | | | | Width: 1629.5 |
|=================================================================================================================================| | Iter | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | result | loss | & validation (sec)| validation loss | validation loss | | | |=================================================================================================================================| | 71 | Accept | 0.16923 | 0.069135 | 0.10154 | 0.11947 | tree | MinLeafSize: 61 |
| 72 | Accept | 0.22769 | 0.060552 | 0.10154 | 0.11947 | nb | DistributionNames: normal | | | | | | | | | Width: NaN |
| 73 | Accept | 0.16308 | 10.133 | 0.10154 | 0.11947 | ensemble | Method: LogitBoost | | | | | | | | | NumLearningCycles: 217 | | | | | | | | | MinLeafSize: 70 |
| 74 | Accept | 0.13231 | 13.055 | 0.10154 | 0.11947 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 257 | | | | | | | | | MinLeafSize: 2 |
| 75 | Accept | 0.21474 | 0.069312 | 0.10154 | 0.11947 | knn | NumNeighbors: 49 |
| 76 | Accept | 0.13846 | 0.083714 | 0.10154 | 0.1214 | tree | MinLeafSize: 25 |
| 77 | Accept | 0.12 | 0.069041 | 0.10154 | 0.11923 | tree | MinLeafSize: 6 |
| 78 | Accept | 0.10154 | 0.077399 | 0.10154 | 0.1118 | tree | MinLeafSize: 5 |
| 79 | Accept | 0.12 | 0.076269 | 0.10154 | 0.11007 | tree | MinLeafSize: 4 |
| 80 | Accept | 0.10154 | 0.09325 | 0.10154 | 0.10878 | tree | MinLeafSize: 5 |
|=================================================================================================================================| | Iter | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | result | loss | & validation (sec)| validation loss | validation loss | | | |=================================================================================================================================| | 81 | Accept | 0.10154 | 0.074541 | 0.10154 | 0.10737 | tree | MinLeafSize: 5 |
| 82 | Accept | 0.12 | 0.069929 | 0.10154 | 0.1063 | tree | MinLeafSize: 4 |
| 83 | Accept | 0.10154 | 0.072591 | 0.10154 | 0.10514 | tree | MinLeafSize: 5 |
| 84 | Accept | 0.10154 | 0.071254 | 0.10154 | 0.10366 | tree | MinLeafSize: 5 |
| 85 | Accept | 0.10154 | 0.077378 | 0.10154 | 0.10361 | tree | MinLeafSize: 5 |
| 86 | Accept | 0.10154 | 0.070643 | 0.10154 | 0.10348 | tree | MinLeafSize: 5 |
| 87 | Accept | 0.12 | 0.070551 | 0.10154 | 0.10286 | tree | MinLeafSize: 4 |
| 88 | Accept | 0.12 | 0.078438 | 0.10154 | 0.1029 | tree | MinLeafSize: 6 |
| 89 | Accept | 0.10154 | 0.072996 | 0.10154 | 0.10262 | tree | MinLeafSize: 5 |
| 90 | Accept | 0.10154 | 0.078162 | 0.10154 | 0.10246 | tree | MinLeafSize: 5 |
|=================================================================================================================================| | Iter | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | result | loss | & validation (sec)| validation loss | validation loss | | | |=================================================================================================================================| | 91 | Accept | 0.10154 | 0.07038 | 0.10154 | 0.10267 | tree | MinLeafSize: 5 |
| 92 | Accept | 0.10154 | 0.07752 | 0.10154 | 0.10257 | tree | MinLeafSize: 5 |
| 93 | Accept | 0.10154 | 0.076155 | 0.10154 | 0.10217 | tree | MinLeafSize: 5 |
| 94 | Accept | 0.10154 | 0.075983 | 0.10154 | 0.10221 | tree | MinLeafSize: 5 |
| 95 | Accept | 0.10154 | 0.07407 | 0.10154 | 0.10211 | tree | MinLeafSize: 5 |
| 96 | Accept | 0.10154 | 0.080633 | 0.10154 | 0.10207 | tree | MinLeafSize: 5 |
| 97 | Accept | 0.10154 | 0.086164 | 0.10154 | 0.10205 | tree | MinLeafSize: 5 |
| 98 | Accept | 0.10154 | 0.080264 | 0.10154 | 0.10191 | tree | MinLeafSize: 5 |
| 99 | Accept | 0.12308 | 0.076015 | 0.10154 | 0.1021 | tree | MinLeafSize: 17 |
| 100 | Accept | 0.10154 | 0.074579 | 0.10154 | 0.1019 | tree | MinLeafSize: 5 |
|=================================================================================================================================| | Iter | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | result | loss | & validation (sec)| validation loss | validation loss | | | |=================================================================================================================================| | 101 | Accept | 0.10154 | 0.072077 | 0.10154 | 0.10186 | tree | MinLeafSize: 5 |
| 102 | Accept | 0.10154 | 0.071287 | 0.10154 | 0.10199 | tree | MinLeafSize: 5 |
| 103 | Accept | 0.10154 | 0.080003 | 0.10154 | 0.10186 | tree | MinLeafSize: 5 |
| 104 | Accept | 0.12 | 0.07462 | 0.10154 | 0.10189 | tree | MinLeafSize: 13 |
| 105 | Accept | 0.10154 | 0.077244 | 0.10154 | 0.10198 | tree | MinLeafSize: 5 |
| 106 | Accept | 0.12 | 0.080302 | 0.10154 | 0.10173 | tree | MinLeafSize: 4 |
| 107 | Accept | 0.10154 | 0.071858 | 0.10154 | 0.10183 | tree | MinLeafSize: 5 |
| 108 | Accept | 0.10154 | 0.076013 | 0.10154 | 0.10166 | tree | MinLeafSize: 5 |
| 109 | Accept | 0.10154 | 0.079106 | 0.10154 | 0.10164 | tree | MinLeafSize: 5 |
| 110 | Accept | 0.10154 | 0.075807 | 0.10154 | 0.10172 | tree | MinLeafSize: 5 |
|=================================================================================================================================| | Iter | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | result | loss | & validation (sec)| validation loss | validation loss | | | |=================================================================================================================================| | 111 | Accept | 0.10154 | 0.076903 | 0.10154 | 0.10159 | tree | MinLeafSize: 5 |
| 112 | Accept | 0.10154 | 0.077409 | 0.10154 | 0.10163 | tree | MinLeafSize: 5 |
| 113 | Accept | 0.10154 | 0.069569 | 0.10154 | 0.10165 | tree | MinLeafSize: 5 |
| 114 | Accept | 0.12308 | 0.076744 | 0.10154 | 0.10156 | tree | MinLeafSize: 18 |
| 115 | Accept | 0.19551 | 0.097443 | 0.10154 | 0.10156 | svm | BoxConstraint: 1.2977 | | | | | | | | | KernelScale: 33.654 |
| 116 | Accept | 0.26603 | 0.099474 | 0.10154 | 0.10156 | svm | BoxConstraint: 0.082725 | | | | | | | | | KernelScale: 139.98 |
| 117 | Accept | 0.37179 | 0.077589 | 0.10154 | 0.10156 | svm | BoxConstraint: 0.0065156 | | | | | | | | | KernelScale: 559.53 |
| 118 | Accept | 0.15385 | 0.08281 | 0.10154 | 0.10156 | svm | BoxConstraint: 8.5337 | | | | | | | | | KernelScale: 482.07 |
| 119 | Accept | 0.20833 | 0.074271 | 0.10154 | 0.10156 | svm | BoxConstraint: 5.1729 | | | | | | | | | KernelScale: 980.38 |
| 120 | Accept | 0.17949 | 0.081488 | 0.10154 | 0.10156 | svm | BoxConstraint: 6.8028 | | | | | | | | | KernelScale: 578.14 |
|=================================================================================================================================| | Iter | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | result | loss | & validation (sec)| validation loss | validation loss | | | |=================================================================================================================================| | 121 | Accept | 0.22115 | 0.076901 | 0.10154 | 0.10156 | svm | BoxConstraint: 0.19345 | | | | | | | | | KernelScale: 367.75 |
| 122 | Accept | 0.17308 | 0.076171 | 0.10154 | 0.10156 | svm | BoxConstraint: 10.344 | | | | | | | | | KernelScale: 679.05 |
| 123 | Accept | 0.125 | 0.09187 | 0.10154 | 0.10156 | svm | BoxConstraint: 72.626 | | | | | | | | | KernelScale: 228.42 |
| 124 | Accept | 0.13462 | 0.15746 | 0.10154 | 0.10156 | svm | BoxConstraint: 586.5 | | | | | | | | | KernelScale: 176.02 |
| 125 | Accept | 0.13462 | 0.093366 | 0.10154 | 0.10156 | svm | BoxConstraint: 83.771 | | | | | | | | | KernelScale: 117.21 |
| 126 | Accept | 0.22436 | 0.10124 | 0.10154 | 0.10156 | svm | BoxConstraint: 962.52 | | | | | | | | | KernelScale: 20.898 |
| 127 | Accept | 0.15705 | 0.1133 | 0.10154 | 0.10156 | svm | BoxConstraint: 29.038 | | | | | | | | | KernelScale: 66.563 |
| 128 | Accept | 0.16346 | 0.10388 | 0.10154 | 0.10156 | svm | BoxConstraint: 156.8 | | | | | | | | | KernelScale: 62.775 |
| 129 | Accept | 0.13782 | 0.087555 | 0.10154 | 0.10156 | svm | BoxConstraint: 94.357 | | | | | | | | | KernelScale: 932.27 |
| 130 | Accept | 0.12821 | 0.093318 | 0.10154 | 0.10156 | svm | BoxConstraint: 25.982 | | | | | | | | | KernelScale: 247.06 |
|=================================================================================================================================| | Iter | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | result | loss | & validation (sec)| validation loss | validation loss | | | |=================================================================================================================================| | 131 | Accept | 0.13462 | 0.08648 | 0.10154 | 0.10156 | svm | BoxConstraint: 16.818 | | | | | | | | | KernelScale: 352.46 |
| 132 | Accept | 0.14103 | 0.090667 | 0.10154 | 0.10156 | svm | BoxConstraint: 15.817 | | | | | | | | | KernelScale: 130.15 |
| 133 | Accept | 0.12179 | 0.089208 | 0.10154 | 0.10156 | svm | BoxConstraint: 74.054 | | | | | | | | | KernelScale: 555.66 |
| 134 | Accept | 0.21474 | 0.082634 | 0.10154 | 0.10156 | svm | BoxConstraint: 0.80097 | | | | | | | | | KernelScale: 239.08 |
| 135 | Accept | 0.125 | 0.088947 | 0.10154 | 0.10156 | svm | BoxConstraint: 47.244 | | | | | | | | | KernelScale: 214.51 |
| 136 | Accept | 0.13141 | 0.07954 | 0.10154 | 0.10156 | svm | BoxConstraint: 13.389 | | | | | | | | | KernelScale: 145.55 |
| 137 | Accept | 0.13782 | 0.091167 | 0.10154 | 0.10156 | svm | BoxConstraint: 60.755 | | | | | | | | | KernelScale: 131.88 |
| 138 | Accept | 0.125 | 0.087338 | 0.10154 | 0.10156 | svm | BoxConstraint: 11.454 | | | | | | | | | KernelScale: 176.64 |
| 139 | Accept | 0.125 | 0.080567 | 0.10154 | 0.10156 | svm | BoxConstraint: 11.85 | | | | | | | | | KernelScale: 160.69 |
| 140 | Accept | 0.16667 | 0.083852 | 0.10154 | 0.10156 | svm | BoxConstraint: 11.155 | | | | | | | | | KernelScale: 652.28 |
|=================================================================================================================================| | Iter | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | result | loss | & validation (sec)| validation loss | validation loss | | | |=================================================================================================================================| | 141 | Accept | 0.125 | 0.089769 | 0.10154 | 0.10156 | svm | BoxConstraint: 10.933 | | | | | | | | | KernelScale: 172.97 |
| 142 | Accept | 0.21474 | 0.1003 | 0.10154 | 0.10156 | svm | BoxConstraint: 16.172 | | | | | | | | | KernelScale: 22.373 |
| 143 | Accept | 0.13782 | 0.0833 | 0.10154 | 0.10156 | svm | BoxConstraint: 7.6332 | | | | | | | | | KernelScale: 178.51 |
| 144 | Accept | 0.13462 | 0.10562 | 0.10154 | 0.10156 | svm | BoxConstraint: 6.7646 | | | | | | | | | KernelScale: 160.72 |
| 145 | Accept | 0.14103 | 0.076631 | 0.10154 | 0.10156 | svm | BoxConstraint: 6.8617 | | | | | | | | | KernelScale: 189.52 |
| 146 | Accept | 0.15385 | 0.082538 | 0.10154 | 0.10156 | svm | BoxConstraint: 6.0384 | | | | | | | | | KernelScale: 332.89 |
| 147 | Accept | 0.14423 | 0.077025 | 0.10154 | 0.10156 | svm | BoxConstraint: 5.7255 | | | | | | | | | KernelScale: 182.19 |
| 148 | Accept | 0.13782 | 0.089308 | 0.10154 | 0.10156 | svm | BoxConstraint: 70.212 | | | | | | | | | KernelScale: 818.46 |
| 149 | Accept | 0.14103 | 0.08348 | 0.10154 | 0.10156 | svm | BoxConstraint: 5.493 | | | | | | | | | KernelScale: 230.68 |
| 150 | Accept | 0.13782 | 0.079744 | 0.10154 | 0.10156 | svm | BoxConstraint: 5.3564 | | | | | | | | | KernelScale: 111.96 |
__________________________________________________________ Optimization completed. Total iterations: 150 Total elapsed time: 835.2167 seconds Total time for training and validation: 193.4979 seconds Best observed learner is a tree model with: MinLeafSize: 5 Observed validation loss: 0.10154 Time for training and validation: 0.079656 seconds Best estimated learner (returned model) is a tree model with: MinLeafSize: 5 Estimated validation loss: 0.10156 Estimated time for training and validation: 0.076205 seconds Documentation for fitcauto display
Последняя модель, возвращенная fitcauto соответствует лучшему предполагаемому ученику. Перед возвращением модели функция переобучает её, используя все данные обучения (carsTrain), перечисленные Learner тип (или модель) и отображаемые значения гиперпараметров.
Оценка производительности тестового набора
Оцените производительность модели в тестовом наборе.
testAccuracy = 1 - loss(Mdl,carsTest,'Origin')testAccuracy = 0.9143
confusionchart(carsTest.Origin,predict(Mdl,carsTest))
Использовать fitcauto автоматически выбирать классификационную модель с оптимизированными гиперпараметрами, заданными данными предиктора и ответа, хранящимися в отдельных переменных.
Загрузить данные
Загрузить humanactivity набор данных. Этот набор данных содержит 24 075 наблюдений за пятью физическими видами деятельности человека: Сидя (1), Стоя (2), Ходьба (3), Бега (4) и Танцы (5). Каждое наблюдение имеет 60 особенностей, извлеченных из данных ускорения, измеренных датчиками акселерометра смартфона. Переменная feat содержит матрицу данных предиктора 60 признаков для 24 075 наблюдений и переменную ответа actid содержит идентификаторы действий для наблюдений в виде целых чисел.
load humanactivityДанные раздела
Разбейте данные на обучающие и тестовые наборы. Используйте 90% наблюдений, чтобы выбрать модель, и 10% наблюдений, чтобы проверить окончательную модель, возвращенную fitcauto. Использовать cvpartition зарезервировать 10% наблюдений для тестирования.
rng('default') % For reproducibility of the partition c = cvpartition(actid,'Holdout',0.10); trainingIndices = training(c); % Indices for the training set XTrain = feat(trainingIndices,:); YTrain = actid(trainingIndices); testIndices = test(c); % Indices for the test set XTest = feat(testIndices,:); YTest = actid(testIndices);
Управляемый fitcauto
Передача данных обучения в fitcauto. По умолчанию fitcauto определяет подходящие типы моделей (или обучающихся) для попытки, использует байесовскую оптимизацию для поиска хороших значений гиперпараметров для этих моделей и возвращает обученную модель с наилучшей ожидаемой производительностью. Укажите, следует ли выполнять оптимизацию параллельно (требуется Toolbox™ параллельных вычислений). Возврат второго выходного сигнала OptimizationResults содержит подробности байесовской оптимизации.
Ожидайте, что этот процесс выбора модели займет некоторое время. По умолчанию fitcauto предоставляет график оптимизации и итеративное отображение результатов оптимизации. Дополнительные сведения о том, как интерпретировать эти результаты, см. в разделе Подробный просмотр.
options = struct('UseParallel',true); [Mdl,OptimizationResults] = fitcauto(XTrain,YTrain,'HyperparameterOptimizationOptions',options);
Warning: It is recommended that you first standardize all numeric predictors when optimizing the Naive Bayes 'Width' parameter. Ignore this warning if you have done that.
Starting parallel pool (parpool) using the 'local' profile ... Connected to the parallel pool (number of workers: 6). Copying objective function to workers... Done copying objective function to workers.
Learner types to explore: ensemble, knn, nb, svm, tree Total iterations (MaxObjectiveEvaluations): 150 Total time (MaxTime): Inf
|===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 1 | 6 | Best | 0.28088 | 48.752 | 0.28088 | 0.28088 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.22686 | | | | | | | | | | KernelScale: 330.4 |
| 2 | 6 | Best | 0.036459 | 51.455 | 0.036459 | 0.036459 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 254 | | | | | | | | | | MinLeafSize: 1786 | | | | | | | | | | MaxNumSplits: 12 |
| 3 | 6 | Best | 0.025845 | 5.1379 | 0.025845 | 0.025845 | tree | MinLeafSize: 59 |
| 4 | 6 | Best | 0.006415 | 60.999 | 0.006415 | 0.021738 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 214 | | | | | | | | | | MinLeafSize: 5 | | | | | | | | | | MaxNumSplits: 23 |
| 5 | 6 | Accept | 0.025845 | 4.8823 | 0.006415 | 0.021738 | tree | MinLeafSize: 59 |
| 6 | 6 | Accept | 0.017768 | 5.4601 | 0.006415 | 0.021738 | tree | MinLeafSize: 9 |
| 7 | 6 | Accept | 0.050212 | 1.1024 | 0.006415 | 0.021738 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |
| 8 | 6 | Accept | 0.050212 | 0.64183 | 0.006415 | 0.021738 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |
| 9 | 6 | Accept | 0.019568 | 151.32 | 0.006415 | 0.021152 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 218 | | | | | | | | | | MinLeafSize: 2 | | | | | | | | | | MaxNumSplits: 63 |
| 10 | 6 | Accept | 0.026537 | 165.42 | 0.006415 | 0.022035 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 264 | | | | | | | | | | MinLeafSize: 7 | | | | | | | | | | MaxNumSplits: 36 |
|===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 11 | 6 | Accept | 0.59166 | 29.107 | 0.006415 | 0.022035 | nb | DistributionNames: kernel | | | | | | | | | | Width: 4.7212e-14 |
| 12 | 6 | Accept | 0.021645 | 50.626 | 0.006415 | 0.022122 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 243 | | | | | | | | | | MinLeafSize: 1247 | | | | | | | | | | MaxNumSplits: 45 |
| 13 | 6 | Accept | 0.043567 | 24.583 | 0.006415 | 0.022122 | knn | NumNeighbors: 144 |
| 14 | 6 | Accept | 0.028844 | 22.503 | 0.006415 | 0.022122 | knn | NumNeighbors: 18 |
| 15 | 6 | Accept | 0.04389 | 157.12 | 0.006415 | 0.022122 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.068467 | | | | | | | | | | KernelScale: 117.01 |
| 16 | 6 | Accept | 0.024598 | 20.721 | 0.006415 | 0.022122 | knn | NumNeighbors: 7 |
| 17 | 6 | Accept | 0.03009 | 20.969 | 0.006415 | 0.022122 | knn | NumNeighbors: 27 |
| 18 | 6 | Accept | 0.016753 | 6.5065 | 0.006415 | 0.021547 | tree | MinLeafSize: 2 |
| 19 | 6 | Accept | 0.040059 | 4.3496 | 0.006415 | 0.022122 | tree | MinLeafSize: 166 |
| 20 | 6 | Accept | 0.060319 | 2.2673 | 0.006415 | 0.022122 | tree | MinLeafSize: 1881 |
|===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 21 | 6 | Accept | 0.050212 | 0.97596 | 0.006415 | 0.022122 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |
| 22 | 6 | Accept | 0.036552 | 20.775 | 0.006415 | 0.022122 | knn | NumNeighbors: 67 |
| 23 | 6 | Accept | 0.050212 | 0.55594 | 0.006415 | 0.022122 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |
| 24 | 6 | Accept | 0.11076 | 36.229 | 0.006415 | 0.022122 | knn | NumNeighbors: 2637 |
| 25 | 6 | Accept | 0.27884 | 66.582 | 0.006415 | 0.024532 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 287 | | | | | | | | | | MinLeafSize: 4344 | | | | | | | | | | MaxNumSplits: 48 |
| 26 | 6 | Accept | 0.58127 | 31.861 | 0.006415 | 0.024532 | nb | DistributionNames: kernel | | | | | | | | | | Width: 1.6293e-06 |
| 27 | 6 | Accept | 0.01583 | 5.7511 | 0.006415 | 0.020656 | tree | MinLeafSize: 1 |
| 28 | 6 | Accept | 0.069319 | 1.806 | 0.006415 | 0.02077 | tree | MinLeafSize: 2284 |
| 29 | 6 | Accept | 0.59166 | 352.55 | 0.006415 | 0.02077 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 790.4 | | | | | | | | | | KernelScale: 0.014348 |
| 30 | 6 | Accept | 0.043336 | 3.7865 | 0.006415 | 0.020133 | tree | MinLeafSize: 432 |
|===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 31 | 6 | Accept | 0.10555 | 34.294 | 0.006415 | 0.020133 | knn | NumNeighbors: 2430 |
| 32 | 6 | Accept | 0.021276 | 4.582 | 0.006415 | 0.018661 | tree | MinLeafSize: 17 |
| 33 | 5 | Accept | 0.030829 | 159.57 | 0.006415 | 0.018642 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 288 | | | | | | | | | | MinLeafSize: 45 | | | | | | | | | | MaxNumSplits: 23 | | 34 | 5 | Accept | 0.014307 | 49.903 | 0.006415 | 0.018642 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 234 | | | | | | | | | | MinLeafSize: 587 | | | | | | | | | | MaxNumSplits: 11 |
| 35 | 5 | Accept | 0.050212 | 1.025 | 0.006415 | 0.018642 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |
| 36 | 6 | Accept | 0.74165 | 24.131 | 0.006415 | 0.018661 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 217 | | | | | | | | | | MinLeafSize: 8856 | | | | | | | | | | MaxNumSplits: 36 |
| 37 | 6 | Accept | 0.4226 | 558.84 | 0.006415 | 0.018661 | nb | DistributionNames: kernel | | | | | | | | | | Width: 72.906 |
| 38 | 6 | Accept | 0.57615 | 317.96 | 0.006415 | 0.018661 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 2.2347 | | | | | | | | | | KernelScale: 0.16176 |
| 39 | 6 | Accept | 0.59166 | 26.64 | 0.006415 | 0.018661 | nb | DistributionNames: kernel | | | | | | | | | | Width: 1.191e-07 |
| 40 | 6 | Accept | 0.087087 | 30.271 | 0.006415 | 0.018661 | knn | NumNeighbors: 1634 |
|===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 41 | 6 | Accept | 0.73985 | 551.47 | 0.006415 | 0.018661 | nb | DistributionNames: kernel | | | | | | | | | | Width: 1055.8 |
| 42 | 4 | Accept | 0.025983 | 146.08 | 0.006415 | 0.018661 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 234 | | | | | | | | | | MinLeafSize: 73 | | | | | | | | | | MaxNumSplits: 43 | | 43 | 4 | Accept | 0.02566 | 146.73 | 0.006415 | 0.018661 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 234 | | | | | | | | | | MinLeafSize: 73 | | | | | | | | | | MaxNumSplits: 43 | | 44 | 4 | Accept | 0.024922 | 124.65 | 0.006415 | 0.018661 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 206 | | | | | | | | | | MinLeafSize: 5 | | | | | | | | | | MaxNumSplits: 39 |
| 45 | 6 | Accept | 0.025891 | 23.638 | 0.006415 | 0.018661 | knn | NumNeighbors: 6 |
| 46 | 5 | Accept | 0.025891 | 24.038 | 0.006415 | 0.018661 | knn | NumNeighbors: 6 | | 47 | 5 | Accept | 0.025891 | 23.646 | 0.006415 | 0.018661 | knn | NumNeighbors: 6 |
| 48 | 6 | Accept | 0.03009 | 188.48 | 0.006415 | 0.018661 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 299 | | | | | | | | | | MinLeafSize: 31 | | | | | | | | | | MaxNumSplits: 25 |
| 49 | 6 | Accept | 0.017214 | 6.0889 | 0.006415 | 0.017935 | tree | MinLeafSize: 4 |
| 50 | 6 | Accept | 0.01726 | 5.6027 | 0.006415 | 0.017303 | tree | MinLeafSize: 5 |
|===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 51 | 6 | Accept | 0.037244 | 158.2 | 0.006415 | 0.017303 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 5.9571 | | | | | | | | | | KernelScale: 840.87 |
| 52 | 6 | Accept | 0.046474 | 190.01 | 0.006415 | 0.017303 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 2.9119 | | | | | | | | | | KernelScale: 12.771 |
| 53 | 6 | Accept | 0.032398 | 155.47 | 0.006415 | 0.017303 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 253 | | | | | | | | | | MinLeafSize: 14 | | | | | | | | | | MaxNumSplits: 22 |
| 54 | 6 | Accept | 0.054135 | 3.0156 | 0.006415 | 0.017093 | tree | MinLeafSize: 783 |
| 55 | 6 | Accept | 0.049797 | 28.421 | 0.006415 | 0.017093 | knn | NumNeighbors: 331 |
| 56 | 6 | Accept | 0.046566 | 27.524 | 0.006415 | 0.017093 | knn | NumNeighbors: 193 |
| 57 | 6 | Accept | 0.36307 | 711.57 | 0.006415 | 0.017093 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.0011107 | | | | | | | | | | KernelScale: 0.80966 |
| 58 | 6 | Accept | 0.022706 | 23.417 | 0.006415 | 0.017093 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 1.3505 | | | | | | | | | | KernelScale: 127.55 |
| 59 | 6 | Accept | 0.028798 | 4.1232 | 0.006415 | 0.01733 | tree | MinLeafSize: 84 |
| 60 | 6 | Accept | 0.041351 | 28.037 | 0.006415 | 0.01733 | knn | NumNeighbors: 124 |
|===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 61 | 6 | Accept | 0.030044 | 26.265 | 0.006415 | 0.01733 | knn | NumNeighbors: 26 |
| 62 | 6 | Accept | 0.11838 | 284.42 | 0.006415 | 0.01733 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.0027874 | | | | | | | | | | KernelScale: 33.944 |
| 63 | 6 | Accept | 0.47116 | 54.038 | 0.006415 | 0.01733 | nb | DistributionNames: kernel | | | | | | | | | | Width: 4.7553e-05 |
| 64 | 6 | Accept | 0.26574 | 104.86 | 0.006415 | 0.01733 | nb | DistributionNames: kernel | | | | | | | | | | Width: 0.0011441 |
| 65 | 6 | Accept | 0.050212 | 0.72243 | 0.006415 | 0.01733 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |
| 66 | 6 | Accept | 0.01726 | 5.6362 | 0.006415 | 0.016846 | tree | MinLeafSize: 5 |
| 67 | 6 | Accept | 0.077072 | 268.17 | 0.006415 | 0.016846 | nb | DistributionNames: kernel | | | | | | | | | | Width: 0.026902 |
| 68 | 6 | Accept | 0.031613 | 25.909 | 0.006415 | 0.016846 | knn | NumNeighbors: 33 |
| 69 | 6 | Accept | 0.02003 | 92.379 | 0.006415 | 0.016846 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 609.47 | | | | | | | | | | KernelScale: 39.88 |
| 70 | 6 | Accept | 0.1145 | 92.795 | 0.006415 | 0.016846 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 292 | | | | | | | | | | MinLeafSize: 3870 | | | | | | | | | | MaxNumSplits: 33 |
|===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 71 | 6 | Accept | 0.012968 | 53.299 | 0.006415 | 0.016846 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 208 | | | | | | | | | | MinLeafSize: 5 | | | | | | | | | | MaxNumSplits: 10 |
| 72 | 6 | Accept | 0.011999 | 53.54 | 0.006415 | 0.016846 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 215 | | | | | | | | | | MinLeafSize: 5 | | | | | | | | | | MaxNumSplits: 11 |
| 73 | 6 | Accept | 0.011999 | 50.887 | 0.006415 | 0.012076 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 205 | | | | | | | | | | MinLeafSize: 2 | | | | | | | | | | MaxNumSplits: 11 |
| 74 | 6 | Accept | 0.039921 | 40.808 | 0.006415 | 0.012733 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 200 | | | | | | | | | | MinLeafSize: 1921 | | | | | | | | | | MaxNumSplits: 11 |
| 75 | 6 | Accept | 0.04232 | 98.684 | 0.006415 | 0.012754 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 220 | | | | | | | | | | MinLeafSize: 1495 | | | | | | | | | | MaxNumSplits: 11 |
| 76 | 6 | Accept | 0.094702 | 397.88 | 0.006415 | 0.012754 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 671.76 | | | | | | | | | | KernelScale: 4.6418 |
| 77 | 6 | Accept | 0.065165 | 44.801 | 0.006415 | 0.013179 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 233 | | | | | | | | | | MinLeafSize: 2586 | | | | | | | | | | MaxNumSplits: 35 |
| 78 | 6 | Accept | 0.22503 | 164.64 | 0.006415 | 0.013179 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 57.71 | | | | | | | | | | KernelScale: 2.0632 |
| 79 | 6 | Accept | 0.03069 | 44.42 | 0.006415 | 0.013027 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 213 | | | | | | | | | | MinLeafSize: 1619 | | | | | | | | | | MaxNumSplits: 95 |
| 80 | 6 | Accept | 0.11459 | 71.56 | 0.006415 | 0.012064 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 212 | | | | | | | | | | MinLeafSize: 2669 | | | | | | | | | | MaxNumSplits: 31 |
|===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 81 | 6 | Accept | 0.044582 | 41.462 | 0.006415 | 0.013176 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 207 | | | | | | | | | | MinLeafSize: 2211 | | | | | | | | | | MaxNumSplits: 56 |
| 82 | 6 | Accept | 0.014722 | 223.7 | 0.006415 | 0.013176 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 832.84 | | | | | | | | | | KernelScale: 392.65 |
| 83 | 6 | Accept | 0.018322 | 23.34 | 0.006415 | 0.013176 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 322.92 | | | | | | | | | | KernelScale: 542.76 |
| 84 | 6 | Accept | 0.016984 | 28.833 | 0.006415 | 0.013176 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 727.84 | | | | | | | | | | KernelScale: 558.25 |
| 85 | 6 | Accept | 0.59166 | 2135.3 | 0.006415 | 0.013176 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.045413 | | | | | | | | | | KernelScale: 0.0034709 |
| 86 | 6 | Accept | 0.012461 | 39.005 | 0.006415 | 0.013176 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 806.52 | | | | | | | | | | KernelScale: 244.64 |
| 87 | 6 | Accept | 0.016753 | 22.649 | 0.006415 | 0.013176 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 173.38 | | | | | | | | | | KernelScale: 329.41 |
| 88 | 6 | Accept | 0.016845 | 28.292 | 0.006415 | 0.013176 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 242.37 | | | | | | | | | | KernelScale: 62.644 |
| 89 | 6 | Accept | 0.016799 | 20.771 | 0.006415 | 0.013176 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 80.674 | | | | | | | | | | KernelScale: 242.97 |
| 90 | 6 | Accept | 0.041259 | 175.27 | 0.006415 | 0.013176 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 2.0147 | | | | | | | | | | KernelScale: 549.95 |
|===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 91 | 6 | Accept | 0.017953 | 19.778 | 0.006415 | 0.013176 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 63.367 | | | | | | | | | | KernelScale: 263.72 |
| 92 | 6 | Accept | 0.019568 | 19.245 | 0.006415 | 0.013176 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 55.612 | | | | | | | | | | KernelScale: 344.11 |
| 93 | 6 | Accept | 0.016061 | 19.511 | 0.006415 | 0.013176 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 60.019 | | | | | | | | | | KernelScale: 185.49 |
| 94 | 6 | Accept | 0.11847 | 149.5 | 0.006415 | 0.013176 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 770.37 | | | | | | | | | | KernelScale: 3.1188 |
| 95 | 6 | Accept | 0.017953 | 18.428 | 0.006415 | 0.013176 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 43.94 | | | | | | | | | | KernelScale: 234.89 |
| 96 | 6 | Accept | 0.025106 | 25.204 | 0.006415 | 0.013176 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 2.1294 | | | | | | | | | | KernelScale: 237.71 |
| 97 | 6 | Accept | 0.011676 | 70.358 | 0.006415 | 0.012446 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 283 | | | | | | | | | | MinLeafSize: 4 | | | | | | | | | | MaxNumSplits: 10 |
| 98 | 6 | Accept | 0.031983 | 37.179 | 0.006415 | 0.012446 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 1.832 | | | | | | | | | | KernelScale: 449.73 |
| 99 | 6 | Accept | 0.0097379 | 76.639 | 0.006415 | 0.011402 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 299 | | | | | | | | | | MinLeafSize: 4 | | | | | | | | | | MaxNumSplits: 12 |
| 100 | 6 | Accept | 0.22416 | 684.65 | 0.006415 | 0.011402 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 632.44 | | | | | | | | | | KernelScale: 1.8647 |
|===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 101 | 6 | Accept | 0.11478 | 87.263 | 0.006415 | 0.011913 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 272 | | | | | | | | | | MinLeafSize: 3176 | | | | | | | | | | MaxNumSplits: 54 |
| 102 | 6 | Accept | 0.0081687 | 75.177 | 0.006415 | 0.01045 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 291 | | | | | | | | | | MinLeafSize: 6 | | | | | | | | | | MaxNumSplits: 14 |
| 103 | 6 | Accept | 0.010753 | 70.01 | 0.006415 | 0.010258 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 284 | | | | | | | | | | MinLeafSize: 1 | | | | | | | | | | MaxNumSplits: 11 |
| 104 | 6 | Accept | 0.36076 | 77 | 0.006415 | 0.010258 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0054235 | | | | | | | | | | KernelScale: 149.67 |
| 105 | 6 | Accept | 0.0084456 | 62.748 | 0.006415 | 0.0092051 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 214 | | | | | | | | | | MinLeafSize: 3 | | | | | | | | | | MaxNumSplits: 16 |
| 106 | 6 | Accept | 0.012738 | 32.34 | 0.006415 | 0.0092051 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 529.51 | | | | | | | | | | KernelScale: 161.83 |
| 107 | 6 | Accept | 0.031521 | 41.482 | 0.006415 | 0.0092051 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.6738 | | | | | | | | | | KernelScale: 289.2 |
| 108 | 6 | Accept | 0.021368 | 38.604 | 0.006415 | 0.0092051 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 567.35 | | | | | | | | | | KernelScale: 36.573 |
| 109 | 6 | Accept | 0.050212 | 0.59557 | 0.006415 | 0.0092051 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |
| 110 | 6 | Accept | 0.57883 | 127.15 | 0.006415 | 0.0092051 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0011506 | | | | | | | | | | KernelScale: 211.98 |
|===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 111 | 6 | Accept | 0.050212 | 0.685 | 0.006415 | 0.0092051 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |
| 112 | 6 | Accept | 0.029583 | 33.504 | 0.006415 | 0.0092051 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.49417 | | | | | | | | | | KernelScale: 209.72 |
| 113 | 6 | Accept | 0.012138 | 41.159 | 0.006415 | 0.0092051 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 967.85 | | | | | | | | | | KernelScale: 153.85 |
| 114 | 6 | Accept | 0.030921 | 38.482 | 0.006415 | 0.0092051 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.57771 | | | | | | | | | | KernelScale: 262.15 |
| 115 | 6 | Accept | 0.1295 | 52.128 | 0.006415 | 0.0092051 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.35331 | | | | | | | | | | KernelScale: 329.77 |
| 116 | 6 | Accept | 0.028475 | 33.553 | 0.006415 | 0.0092051 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.37306 | | | | | | | | | | KernelScale: 157.01 |
| 117 | 6 | Accept | 0.050212 | 0.62316 | 0.006415 | 0.0092051 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |
| 118 | 6 | Accept | 0.030737 | 37 | 0.006415 | 0.0092051 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.37996 | | | | | | | | | | KernelScale: 204.24 |
| 119 | 6 | Accept | 0.021229 | 36.97 | 0.006415 | 0.0092051 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 803.51 | | | | | | | | | | KernelScale: 38.271 |
| 120 | 6 | Accept | 0.015184 | 24.004 | 0.006415 | 0.0092051 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 119.72 | | | | | | | | | | KernelScale: 145.99 |
|===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 121 | 6 | Accept | 0.06955 | 101.38 | 0.006415 | 0.0092051 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 39.152 | | | | | | | | | | KernelScale: 8.5572 |
| 122 | 6 | Accept | 0.028291 | 32.637 | 0.006415 | 0.0092051 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.32264 | | | | | | | | | | KernelScale: 134.73 |
| 123 | 6 | Accept | 0.041951 | 51.65 | 0.006415 | 0.0092051 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.28203 | | | | | | | | | | KernelScale: 237.1 |
| 124 | 6 | Accept | 0.039921 | 216.38 | 0.006415 | 0.0092051 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.049329 | | | | | | | | | | KernelScale: 190.24 |
| 125 | 6 | Accept | 0.048828 | 91.888 | 0.006415 | 0.0092051 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 180.12 | | | | | | | | | | KernelScale: 12.235 |
| 126 | 6 | Accept | 0.030644 | 38.563 | 0.006415 | 0.0092051 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.25725 | | | | | | | | | | KernelScale: 171.19 |
| 127 | 6 | Accept | 0.029537 | 37.409 | 0.006415 | 0.0092051 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.25269 | | | | | | | | | | KernelScale: 145.85 |
| 128 | 6 | Accept | 0.02949 | 31.222 | 0.006415 | 0.0092051 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.24034 | | | | | | | | | | KernelScale: 59.755 |
| 129 | 6 | Accept | 0.029537 | 130.19 | 0.006415 | 0.0092051 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 76.533 | | | | | | | | | | KernelScale: 22.982 |
| 130 | 6 | Accept | 0.094702 | 174.14 | 0.006415 | 0.0092051 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.043666 | | | | | | | | | | KernelScale: 25.411 |
|===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 131 | 6 | Accept | 0.037382 | 46.207 | 0.006415 | 0.0092051 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.23867 | | | | | | | | | | KernelScale: 204.62 |
| 132 | 6 | Accept | 0.022152 | 40.588 | 0.006415 | 0.0092051 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 887.62 | | | | | | | | | | KernelScale: 32.987 |
| 133 | 6 | Accept | 0.032629 | 32.651 | 0.006415 | 0.0092051 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.22511 | | | | | | | | | | KernelScale: 52.294 |
| 134 | 6 | Accept | 0.02806 | 131.12 | 0.006415 | 0.0092051 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.20346 | | | | | | | | | | KernelScale: 122.85 |
| 135 | 6 | Accept | 0.59166 | 3480 | 0.006415 | 0.0092051 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 658.37 | | | | | | | | | | KernelScale: 0.0016161 |
| 136 | 6 | Accept | 0.59166 | 3481.3 | 0.006415 | 0.0092051 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 658.37 | | | | | | | | | | KernelScale: 0.0016161 |
| 137 | 6 | Accept | 0.032121 | 44.172 | 0.006415 | 0.0092051 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.20648 | | | | | | | | | | KernelScale: 162.33 |
| 138 | 6 | Accept | 0.30755 | 71.9 | 0.006415 | 0.0092051 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.015886 | | | | | | | | | | KernelScale: 120.23 |
| 139 | 6 | Accept | 0.040936 | 233.89 | 0.006415 | 0.0092051 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.046614 | | | | | | | | | | KernelScale: 213.78 |
| 140 | 5 | Accept | 0.040521 | 224.72 | 0.006415 | 0.0092051 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.043998 | | | | | | | | | | KernelScale: 195.58 | |===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 141 | 5 | Accept | 0.22208 | 64.659 | 0.006415 | 0.0092051 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.049755 | | | | | | | | | | KernelScale: 151.01 |
| 142 | 6 | Accept | 0.029444 | 30.014 | 0.006415 | 0.0092051 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.21789 | | | | | | | | | | KernelScale: 64.023 |
| 143 | 6 | Accept | 0.046013 | 49.025 | 0.006415 | 0.0092051 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.14805 | | | | | | | | | | KernelScale: 178.98 |
| 144 | 6 | Accept | 0.013291 | 42.726 | 0.006415 | 0.0092051 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 960.14 | | | | | | | | | | KernelScale: 334.83 |
| 145 | 6 | Accept | 0.59166 | 3493.5 | 0.006415 | 0.0092051 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 17.982 | | | | | | | | | | KernelScale: 0.0043756 |
| 146 | 6 | Accept | 0.089533 | 189.71 | 0.006415 | 0.0092051 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.2326 | | | | | | | | | | KernelScale: 15.411 |
| 147 | 6 | Accept | 0.03369 | 49.586 | 0.006415 | 0.0092051 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.21482 | | | | | | | | | | KernelScale: 180.5 |
| 148 | 6 | Accept | 0.013384 | 42.876 | 0.006415 | 0.0092051 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 920.16 | | | | | | | | | | KernelScale: 334.75 |
| 149 | 6 | Accept | 0.027368 | 117.6 | 0.006415 | 0.0092051 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.22783 | | | | | | | | | | KernelScale: 105.59 |
| 150 | 6 | Accept | 0.041674 | 234.61 | 0.006415 | 0.0092051 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.032335 | | | | | | | | | | KernelScale: 182.17 |
__________________________________________________________ Optimization completed. Total iterations: 150 Total elapsed time: 4534.2478 seconds Total time for training and validation: 24883.8563 seconds Best observed learner is an ensemble model with: Method: AdaBoostM2 NumLearningCycles: 214 MinLeafSize: 5 MaxNumSplits: 23 Observed validation loss: 0.006415 Time for training and validation: 60.9987 seconds Best estimated learner (returned model) is an ensemble model with: Method: AdaBoostM2 NumLearningCycles: 214 MinLeafSize: 3 MaxNumSplits: 16 Estimated validation loss: 0.0092051 Estimated time for training and validation: 57.8146 seconds Documentation for fitcauto display
Последняя модель, возвращенная fitcauto соответствует лучшему предполагаемому ученику. Перед возвращением модели функция переобучает её, используя все данные обучения (XTrain и YTrain), перечисленные Learner тип (или модель) и отображаемые значения гиперпараметров.
Оценка производительности тестового набора
Оцените конечную производительность модели в наборе данных теста.
testAccuracy = 1 - loss(Mdl,XTest,YTest)
testAccuracy = 0.9917
Окончательная модель правильно классифицирует более 99% наблюдений.
Использовать fitcauto для автоматического выбора классификационной модели с оптимизированными гиперпараметрами, заданными данными предиктора и ответа, хранящимися в таблице. Перед передачей данных fitcauto, выберите функцию, чтобы удалить из набора данных неважные предикторы.
Загрузка и разбиение данных
Прочитать образец файла CreditRating_Historical.dat в таблицу. Данные предиктора состоят из финансовых коэффициентов и отраслевой информации для списка корпоративных клиентов. Переменная ответа состоит из кредитных рейтингов, присвоенных рейтинговым агентством. Предварительный просмотр первых нескольких строк набора данных.
creditrating = readtable('CreditRating_Historical.dat');
head(creditrating)ans=8×8 table
ID WC_TA RE_TA EBIT_TA MVE_BVTD S_TA Industry Rating
_____ ______ ______ _______ ________ _____ ________ _______
62394 0.013 0.104 0.036 0.447 0.142 3 {'BB' }
48608 0.232 0.335 0.062 1.969 0.281 8 {'A' }
42444 0.311 0.367 0.074 1.935 0.366 1 {'A' }
48631 0.194 0.263 0.062 1.017 0.228 4 {'BBB'}
43768 0.121 0.413 0.057 3.647 0.466 12 {'AAA'}
39255 -0.117 -0.799 0.01 0.179 0.082 4 {'CCC'}
62236 0.087 0.158 0.049 0.816 0.324 2 {'BBB'}
39354 0.005 0.181 0.034 2.597 0.388 7 {'AA' }
Потому что каждое значение в ID переменная является уникальным идентификатором клиента, то есть length(unique(creditrating.ID)) равно количеству наблюдений в creditrating, ID переменная является плохим предиктором. Удалить ID переменную из таблицы и преобразовать Industry переменной к categorical переменная.
creditrating = removevars(creditrating,'ID');
creditrating.Industry = categorical(creditrating.Industry);Разбейте данные на обучающие и тестовые наборы. Используйте приблизительно 85% наблюдений для процесса выбора модели и настройки гиперпараметров, а 15% наблюдений для проверки производительности окончательной модели, возвращенной fitcauto по новым данным. Использовать cvpartition для секционирования данных.
rng('default') % For reproducibility of the partition c = cvpartition(creditrating.Rating,'Holdout',0.15); trainingIndices = training(c); % Indices for the training set testIndices = test(c); % Indices for the test set creditTrain = creditrating(trainingIndices,:); creditTest = creditrating(testIndices,:);
Выполнение выбора элементов
Перед передачей данных обучения fitcauto, найдите важные предикторы, используя fscchi2 функция. Визуализация показателей предиктора с помощью bar функция. Потому что некоторые оценки могут быть Inf, и bar браки Inf значения, сначала постройте график конечных баллов, а затем постройте график конечного представления Inf оценки в другом цвете.
[idx,scores] = fscchi2(creditTrain,'Rating'); bar(scores(idx)) % Represents finite scores hold on veryImportant = isinf(scores); finiteMax = max(scores(~veryImportant)); bar(finiteMax*veryImportant(idx)) % Represents Inf scores hold off xticklabels(strrep(creditTrain.Properties.VariableNames(idx),'_','\_')) xtickangle(45) legend({'Finite Scores','Inf Scores'})
Обратите внимание, что Industry предиктор имеет низкий балл, соответствующий значению p, которое больше 0,05, что указывает на то, что Industry возможно, это не является важной особенностью. Удалить Industry из наборов обучающих и тестовых данных.
creditTrain = removevars(creditTrain,'Industry'); creditTest = removevars(creditTest,'Industry');
Управляемый fitcauto
Передача данных обучения в fitcauto. Функция использует байесовскую оптимизацию для выбора моделей и их значений гиперпараметров и возвращает обученную модель Mdl с наилучшей ожидаемой производительностью. Укажите, чтобы попробовать все доступные типы учащихся и запустить оптимизацию параллельно (требуется Parallel Computing Toolbox™). Возврат второго выходного сигнала Results содержит подробности байесовской оптимизации.
Ожидайте, что этот процесс займет некоторое время. По умолчанию fitcauto предоставляет график оптимизации и итеративное отображение результатов оптимизации. Дополнительные сведения о том, как интерпретировать эти результаты, см. в разделе Подробный просмотр.
options = struct('UseParallel',true); [Mdl,Results] = fitcauto(creditTrain,'Rating', ... 'Learners','all','HyperparameterOptimizationOptions',options);
Warning: It is recommended that you first standardize all numeric predictors when optimizing the Naive Bayes 'Width' parameter. Ignore this warning if you have done that.
Starting parallel pool (parpool) using the 'local' profile ... Connected to the parallel pool (number of workers: 6).
Copying objective function to workers... Done copying objective function to workers.
Learner types to explore: discr, ensemble, kernel, knn, linear, nb, svm, tree Total iterations (MaxObjectiveEvaluations): 240 Total time (MaxTime): Inf
|===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 1 | 6 | Best | 0.42716 | 3.0379 | 0.42716 | 0.42716 | discr | Delta: 0.00046441 | | | | | | | | | | Gamma: 0.2485 |
| 2 | 4 | Accept | 0.74185 | 4.7899 | 0.24948 | 0.29794 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.48455 | | | | | | | | | | KernelScale: 354.44 | | 3 | 4 | Best | 0.24948 | 5.0813 | 0.24948 | 0.29794 | linear | Coding: onevsone | | | | | | | | | | Lambda: 6.3551e-08 | | | | | | | | | | Learner: logistic | | 4 | 4 | Accept | 0.29794 | 3.7295 | 0.24948 | 0.29794 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 12 | | | | | | | | | | LearnRate: 0.063776 | | | | | | | | | | MinLeafSize: 277 |
| 5 | 3 | Accept | 0.25097 | 9.2655 | 0.24948 | 0.25067 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 7.8433 | | | | | | | | | | Lambda: 1.4468e-06 | | 6 | 3 | Accept | 0.25067 | 0.81139 | 0.24948 | 0.25067 | knn | NumNeighbors: 105 | | | | | | | | | | Distance: minkowski |
| 7 | 6 | Accept | 0.52917 | 2.3362 | 0.24948 | 0.25067 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.002417 | | | | | | | | | | KernelScale: 356.9 |
| 8 | 3 | Accept | 0.55818 | 0.63908 | 0.24948 | 0.25067 | discr | Delta: 0.98612 | | | | | | | | | | Gamma: 0.86519 | | 9 | 3 | Accept | 0.3781 | 1.6777 | 0.24948 | 0.25067 | linear | Coding: onevsall | | | | | | | | | | Lambda: 1.0412e-06 | | | | | | | | | | Learner: logistic | | 10 | 3 | Accept | 0.43225 | 0.80766 | 0.24948 | 0.25067 | discr | Delta: 0.00013711 | | | | | | | | | | Gamma: 0.60585 | |===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 11 | 3 | Accept | 0.47712 | 3.3756 | 0.24948 | 0.25067 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 2.7347 | | | | | | | | | | KernelScale: 24.465 |
| 12 | 6 | Accept | 0.25695 | 2.3709 | 0.24948 | 0.25067 | nb | DistributionNames: kernel | | | | | | | | | | Width: 0.057566 |
| 13 | 4 | Accept | 0.26413 | 0.49941 | 0.24379 | 0.25067 | tree | MinLeafSize: 30 | | 14 | 4 | Accept | 0.42327 | 0.85101 | 0.24379 | 0.25067 | knn | NumNeighbors: 56 | | | | | | | | | | Distance: cosine | | 15 | 4 | Best | 0.24379 | 1.8084 | 0.24379 | 0.25067 | linear | Coding: onevsone | | | | | | | | | | Lambda: 5.9172e-05 | | | | | | | | | | Learner: svm |
| 16 | 3 | Accept | 0.81544 | 4.9586 | 0.24379 | 0.25067 | kernel | Coding: onevsall | | | | | | | | | | KernelScale: 0.0043375 | | | | | | | | | | Lambda: 0.0023789 | | 17 | 3 | Accept | 0.45169 | 0.96723 | 0.24379 | 0.25067 | linear | Coding: onevsall | | | | | | | | | | Lambda: 0.0028505 | | | | | | | | | | Learner: svm |
| 18 | 6 | Accept | 0.33712 | 0.19972 | 0.24379 | 0.25695 | knn | NumNeighbors: 1 | | | | | | | | | | Distance: cityblock |
| 19 | 3 | Accept | 0.4834 | 0.38951 | 0.24379 | 0.25695 | knn | NumNeighbors: 72 | | | | | | | | | | Distance: correlation | | 20 | 3 | Accept | 0.46336 | 0.78881 | 0.24379 | 0.25695 | linear | Coding: onevsall | | | | | | | | | | Lambda: 0.0075732 | | | | | | | | | | Learner: svm | |===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 21 | 3 | Accept | 0.82082 | 2.9223 | 0.24379 | 0.25695 | kernel | Coding: onevsall | | | | | | | | | | KernelScale: 0.0042587 | | | | | | | | | | Lambda: 0.0014754 | | 22 | 3 | Accept | 0.61292 | 1.8557 | 0.24379 | 0.25695 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 13 | | | | | | | | | | LearnRate: 0.055349 | | | | | | | | | | MinLeafSize: 910 |
| 23 | 6 | Accept | 0.43255 | 0.68689 | 0.24379 | 0.25695 | discr | Delta: 0.016844 | | | | | | | | | | Gamma: 0.64466 |
| 24 | 4 | Accept | 0.28866 | 1.9017 | 0.24379 | 0.25695 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 10 | | | | | | | | | | LearnRate: 0.11662 | | | | | | | | | | MinLeafSize: 181 | | 25 | 4 | Accept | 0.74185 | 1.3546 | 0.24379 | 0.25695 | knn | NumNeighbors: 1314 | | | | | | | | | | Distance: hamming | | 26 | 4 | Accept | 0.42746 | 0.69002 | 0.24379 | 0.25695 | discr | Delta: 2.2544e-06 | | | | | | | | | | Gamma: 0.87275 |
| 27 | 3 | Accept | 0.25606 | 12.143 | 0.24379 | 0.25498 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 132 | | | | | | | | | | LearnRate: 0.92674 | | | | | | | | | | MinLeafSize: 127 | | 28 | 3 | Accept | 0.25366 | 2.4732 | 0.24379 | 0.25498 | nb | DistributionNames: kernel | | | | | | | | | | Width: 0.10033 |
| 29 | 6 | Accept | 0.66796 | 0.22938 | 0.24379 | 0.25498 | knn | NumNeighbors: 77 | | | | | | | | | | Distance: jaccard |
| 30 | 4 | Accept | 0.69488 | 1.9352 | 0.242 | 0.25498 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 8.4886 | | | | | | | | | | KernelScale: 192.19 | |===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 31 | 4 | Best | 0.242 | 1.9467 | 0.242 | 0.25498 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 5.425 | | | | | | | | | | KernelScale: 2.434 | | 32 | 4 | Accept | 0.32306 | 0.48104 | 0.242 | 0.25498 | tree | MinLeafSize: 2 |
| 33 | 4 | Accept | 0.43225 | 0.10463 | 0.242 | 0.25498 | discr | Delta: 0.00015292 | | | | | | | | | | Gamma: 0.51045 |
| 34 | 4 | Accept | 0.32994 | 0.2065 | 0.242 | 0.25498 | tree | MinLeafSize: 3 |
| 35 | 6 | Accept | 0.53814 | 2.8748 | 0.242 | 0.25498 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 6.8148 | | | | | | | | | | KernelScale: 382.11 |
| 36 | 4 | Accept | 0.24529 | 105.62 | 0.242 | 0.25498 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.25488 | | | | | | | | | | KernelScale: 0.0037823 | | 37 | 4 | Accept | 0.53814 | 3.3272 | 0.242 | 0.25498 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 6.8148 | | | | | | | | | | KernelScale: 382.11 | | 38 | 4 | Accept | 0.53814 | 3.8593 | 0.242 | 0.25498 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 6.8148 | | | | | | | | | | KernelScale: 382.11 |
| 39 | 4 | Accept | 0.25965 | 14.211 | 0.242 | 0.25498 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 150 | | | | | | | | | | LearnRate: 0.014842 | | | | | | | | | | MinLeafSize: 21 |
| 40 | 5 | Accept | 0.42656 | 0.16731 | 0.242 | 0.25498 | discr | Delta: 0.0020866 | | | | | | | | | | Gamma: 0.091054 |
|===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 41 | 5 | Accept | 0.42656 | 0.11476 | 0.242 | 0.25498 | discr | Delta: 0.0020866 | | | | | | | | | | Gamma: 0.091054 |
| 42 | 4 | Accept | 0.2767 | 21.389 | 0.242 | 0.25498 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 221 | | | | | | | | | | LearnRate: 0.0028588 | | | | | | | | | | MinLeafSize: 1 | | 43 | 4 | Accept | 0.29973 | 0.1848 | 0.242 | 0.25498 | tree | MinLeafSize: 7 |
| 44 | 4 | Accept | 0.25935 | 20.084 | 0.242 | 0.25498 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 304 | | | | | | | | | | LearnRate: NaN | | | | | | | | | | MinLeafSize: 100 |
| 45 | 3 | Accept | 0.24499 | 6.5071 | 0.242 | 0.26328 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.019387 | | | | | | | | | | KernelScale: 0.0047515 | | 46 | 3 | Accept | 0.28059 | 0.19378 | 0.242 | 0.26328 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |
| 47 | 6 | Accept | 0.27281 | 0.13181 | 0.242 | 0.26328 | knn | NumNeighbors: 8 | | | | | | | | | | Distance: chebychev |
| 48 | 3 | Accept | 0.2429 | 1.5474 | 0.242 | 0.26328 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.16719 | | | | | | | | | | KernelScale: 0.11257 | | 49 | 3 | Accept | 0.2423 | 1.5219 | 0.242 | 0.26328 | linear | Coding: onevsone | | | | | | | | | | Lambda: 4.28e-07 | | | | | | | | | | Learner: svm | | 50 | 3 | Accept | 0.67125 | 0.68464 | 0.242 | 0.26328 | knn | NumNeighbors: 86 | | | | | | | | | | Distance: jaccard | |===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 51 | 3 | Accept | 0.46485 | 0.8417 | 0.242 | 0.26328 | tree | MinLeafSize: 645 |
| 52 | 6 | Accept | 0.63147 | 3.1926 | 0.242 | 0.26328 | kernel | Coding: onevsall | | | | | | | | | | KernelScale: 176.2 | | | | | | | | | | Lambda: 2.3903e-06 |
| 53 | 4 | Accept | 0.37571 | 0.11086 | 0.242 | 0.26706 | tree | MinLeafSize: 473 | | 54 | 4 | Accept | 0.29136 | 0.43908 | 0.242 | 0.26706 | knn | NumNeighbors: 354 | | | | | | | | | | Distance: euclidean | | 55 | 4 | Accept | 0.28059 | 0.50642 | 0.242 | 0.26706 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |
| 56 | 3 | Accept | 0.36375 | 5.2617 | 0.242 | 0.26706 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 28.598 | | | | | | | | | | Lambda: 8.3238e-05 | | 57 | 3 | Accept | 0.27251 | 0.13425 | 0.242 | 0.26706 | tree | MinLeafSize: 20 |
| 58 | 6 | Accept | 0.43225 | 0.083255 | 0.242 | 0.26706 | discr | Delta: 0.021467 | | | | | | | | | | Gamma: 0.66016 |
| 59 | 3 | Accept | 0.28059 | 0.10921 | 0.242 | 0.26106 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN | | 60 | 3 | Accept | 0.42537 | 0.15885 | 0.242 | 0.26106 | discr | Delta: 0.001728 | | | | | | | | | | Gamma: 0.89471 | |===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 61 | 3 | Accept | 0.81484 | 2.9209 | 0.242 | 0.26106 | kernel | Coding: onevsall | | | | | | | | | | KernelScale: 0.0063987 | | | | | | | | | | Lambda: 0.0075855 | | 62 | 3 | Accept | 0.24948 | 2.4493 | 0.242 | 0.26106 | linear | Coding: onevsone | | | | | | | | | | Lambda: 1.3237e-07 | | | | | | | | | | Learner: logistic |
| 63 | 6 | Accept | 0.68531 | 0.40944 | 0.242 | 0.26106 | knn | NumNeighbors: 260 | | | | | | | | | | Distance: jaccard |
| 64 | 3 | Accept | 0.32426 | 9.8071 | 0.242 | 0.26007 | ensemble | Method: RUSBoost | | | | | | | | | | NumLearningCycles: 132 | | | | | | | | | | LearnRate: 0.0014516 | | | | | | | | | | MinLeafSize: 104 | | 65 | 3 | Accept | 0.55369 | 0.69919 | 0.242 | 0.26007 | knn | NumNeighbors: 615 | | | | | | | | | | Distance: correlation | | 66 | 3 | Accept | 0.24319 | 1.5742 | 0.242 | 0.26007 | linear | Coding: onevsone | | | | | | | | | | Lambda: 4.3001e-09 | | | | | | | | | | Learner: svm | | 67 | 3 | Accept | 0.70894 | 1.9822 | 0.242 | 0.26007 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.45564 | | | | | | | | | | KernelScale: 51.195 |
| 68 | 6 | Accept | 0.46485 | 0.17082 | 0.242 | 0.26007 | tree | MinLeafSize: 611 |
| 69 | 5 | Accept | 0.74185 | 2.1172 | 0.242 | 0.26007 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.94197 | | | | | | | | | | KernelScale: 524.56 | | 70 | 5 | Accept | 0.83548 | 6.3585 | 0.242 | 0.26007 | kernel | Coding: onevsall | | | | | | | | | | KernelScale: 0.0038762 | | | | | | | | | | Lambda: 2.288e-06 |
|===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 71 | 3 | Accept | 0.25905 | 16.096 | 0.242 | 0.26007 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 160 | | | | | | | | | | LearnRate: NaN | | | | | | | | | | MinLeafSize: 2 | | 72 | 3 | Accept | 0.28448 | 13.826 | 0.242 | 0.26007 | ensemble | Method: RUSBoost | | | | | | | | | | NumLearningCycles: 161 | | | | | | | | | | LearnRate: 0.7581 | | | | | | | | | | MinLeafSize: 14 | | 73 | 3 | Accept | 0.25695 | 0.24642 | 0.242 | 0.26007 | knn | NumNeighbors: 14 | | | | | | | | | | Distance: cityblock |
| 74 | 6 | Accept | 0.32396 | 1.8799 | 0.242 | 0.26007 | kernel | Coding: onevsall | | | | | | | | | | KernelScale: 0.86904 | | | | | | | | | | Lambda: 0.29724 |
| 75 | 4 | Accept | 0.32456 | 0.20481 | 0.242 | 0.26007 | tree | MinLeafSize: 5 | | 76 | 4 | Accept | 0.32994 | 0.35799 | 0.242 | 0.26007 | tree | MinLeafSize: 3 | | 77 | 4 | Accept | 0.26054 | 4.1734 | 0.242 | 0.26007 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 32 | | | | | | | | | | LearnRate: 0.06853 | | | | | | | | | | MinLeafSize: 19 |
| 78 | 4 | Accept | 0.43703 | 0.92964 | 0.242 | 0.24831 | linear | Coding: onevsall | | | | | | | | | | Lambda: 0.013265 | | | | | | | | | | Learner: logistic |
| 79 | 4 | Accept | 0.31588 | 17.116 | 0.242 | 0.24831 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 201 | | | | | | | | | | LearnRate: 0.0012955 | | | | | | | | | | MinLeafSize: 319 |
| 80 | 3 | Accept | 0.25277 | 7.8173 | 0.242 | 0.24831 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 0.78697 | | | | | | | | | | Lambda: 4.1197e-06 | |===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 81 | 3 | Accept | 0.43015 | 0.10239 | 0.242 | 0.24831 | discr | Delta: 0.0069822 | | | | | | | | | | Gamma: 0.49526 |
| 82 | 6 | Accept | 0.42208 | 0.096953 | 0.242 | 0.24831 | discr | Delta: 0.057485 | | | | | | | | | | Gamma: 0.045714 |
| 83 | 3 | Accept | 0.52617 | 2.3915 | 0.242 | 0.24831 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.26869 | | | | | | | | | | KernelScale: 17.595 | | 84 | 3 | Accept | 0.43344 | 1.2875 | 0.242 | 0.24831 | knn | NumNeighbors: 119 | | | | | | | | | | Distance: mahalanobis | | 85 | 3 | Accept | 0.30093 | 1.3003 | 0.242 | 0.24831 | linear | Coding: onevsone | | | | | | | | | | Lambda: 0.047624 | | | | | | | | | | Learner: svm | | 86 | 3 | Accept | 0.42267 | 0.64506 | 0.242 | 0.24831 | knn | NumNeighbors: 48 | | | | | | | | | | Distance: cosine |
| 87 | 6 | Accept | 0.32905 | 0.26891 | 0.242 | 0.24831 | knn | NumNeighbors: 65 | | | | | | | | | | Distance: seuclidean |
| 88 | 4 | Accept | 0.24349 | 1.9282 | 0.242 | 0.24684 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0024196 | | | | | | | | | | KernelScale: 0.0082547 | | 89 | 4 | Accept | 0.24499 | 1.6543 | 0.242 | 0.24684 | linear | Coding: onevsone | | | | | | | | | | Lambda: 3.3697e-06 | | | | | | | | | | Learner: svm | | 90 | 4 | Accept | 0.24469 | 2.1463 | 0.242 | 0.24684 | linear | Coding: onevsone | | | | | | | | | | Lambda: 6.5777e-05 | | | | | | | | | | Learner: logistic |
|===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 91 | 4 | Accept | 0.28059 | 0.15481 | 0.242 | 0.24684 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |
| 92 | 5 | Accept | 0.28059 | 0.38138 | 0.242 | 0.24684 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN | | 93 | 5 | Accept | 0.28059 | 0.15804 | 0.242 | 0.24684 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |
| 94 | 4 | Accept | 0.29674 | 11.234 | 0.242 | 0.24684 | ensemble | Method: RUSBoost | | | | | | | | | | NumLearningCycles: 147 | | | | | | | | | | LearnRate: 0.95321 | | | | | | | | | | MinLeafSize: 112 | | 95 | 4 | Accept | 0.28059 | 0.12279 | 0.242 | 0.24684 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |
| 96 | 4 | Accept | 0.29704 | 1.1016 | 0.242 | 0.24684 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 11 | | | | | | | | | | LearnRate: 0.0072731 | | | | | | | | | | MinLeafSize: 12 |
| 97 | 4 | Accept | 0.28059 | 0.099833 | 0.242 | 0.24684 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |
| 98 | 4 | Best | 0.2411 | 1.3634 | 0.2411 | 0.24471 | linear | Coding: onevsone | | | | | | | | | | Lambda: 0.00056045 | | | | | | | | | | Learner: svm |
| 99 | 4 | Accept | 0.74185 | 0.10441 | 0.2411 | 0.24471 | discr | Delta: 39.281 | | | | | | | | | | Gamma: 0.77032 |
| 100 | 4 | Accept | 0.30093 | 0.11043 | 0.2411 | 0.24471 | tree | MinLeafSize: 135 |
|===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 101 | 4 | Accept | 0.46844 | 0.17182 | 0.2411 | 0.24471 | knn | NumNeighbors: 2 | | | | | | | | | | Distance: cosine |
| 102 | 4 | Accept | 0.25426 | 3.1634 | 0.2411 | 0.24471 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 38 | | | | | | | | | | LearnRate: NaN | | | | | | | | | | MinLeafSize: 28 |
| 103 | 4 | Accept | 0.24469 | 1.6558 | 0.2411 | 0.24558 | linear | Coding: onevsone | | | | | | | | | | Lambda: 0.0012413 | | | | | | | | | | Learner: logistic |
| 104 | 4 | Accept | 0.43823 | 3.6572 | 0.2411 | 0.24558 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 13.293 | | | | | | | | | | Lambda: 0.0031304 |
| 105 | 4 | Accept | 0.31977 | 1.3423 | 0.2411 | 0.24556 | linear | Coding: onevsone | | | | | | | | | | Lambda: 0.040102 | | | | | | | | | | Learner: logistic |
| 106 | 6 | Accept | 0.24678 | 2.0849 | 0.2411 | 0.24529 | linear | Coding: onevsone | | | | | | | | | | Lambda: 3.9918e-05 | | | | | | | | | | Learner: logistic |
| 107 | 5 | Accept | 0.24678 | 1.9627 | 0.2411 | 0.24441 | linear | Coding: onevsone | | | | | | | | | | Lambda: 3.9918e-05 | | | | | | | | | | Learner: logistic | | 108 | 5 | Accept | 0.24678 | 2.3413 | 0.2411 | 0.24441 | linear | Coding: onevsone | | | | | | | | | | Lambda: 3.9918e-05 | | | | | | | | | | Learner: logistic |
| 109 | 4 | Accept | 0.29435 | 14.698 | 0.2411 | 0.24441 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 249 | | | | | | | | | | LearnRate: NaN | | | | | | | | | | MinLeafSize: 292 | | 110 | 4 | Accept | 0.37152 | 0.098701 | 0.2411 | 0.24441 | tree | MinLeafSize: 375 |
|===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 111 | 4 | Accept | 0.25666 | 5.7386 | 0.2411 | 0.24441 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 61 | | | | | | | | | | LearnRate: NaN | | | | | | | | | | MinLeafSize: 3 | | 112 | 4 | Accept | 0.28059 | 0.10645 | 0.2411 | 0.24441 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |
| 113 | 4 | Accept | 0.28059 | 0.10515 | 0.2411 | 0.24441 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |
| 114 | 6 | Accept | 0.74185 | 2.7447 | 0.2411 | 0.24441 | nb | DistributionNames: kernel | | | | | | | | | | Width: 74.975 |
| 115 | 4 | Accept | 0.78552 | 10.495 | 0.2411 | 0.24441 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 0.0050713 | | | | | | | | | | Lambda: 3.7406e-06 | | 116 | 4 | Accept | 0.74185 | 2.7544 | 0.2411 | 0.24441 | nb | DistributionNames: kernel | | | | | | | | | | Width: 74.975 | | 117 | 4 | Accept | 0.74185 | 2.6565 | 0.2411 | 0.24441 | nb | DistributionNames: kernel | | | | | | | | | | Width: 74.975 |
| 118 | 4 | Accept | 0.45797 | 11.614 | 0.2411 | 0.24441 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.94716 | | | | | | | | | | KernelScale: 0.072905 |
| 119 | 4 | Accept | 0.65271 | 1.6037 | 0.2411 | 0.24441 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0010356 | | | | | | | | | | KernelScale: 1.3521 |
| 120 | 4 | Accept | 0.3108 | 1.4953 | 0.2411 | 0.24441 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.080708 | | | | | | | | | | KernelScale: 2.7439 |
|===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 121 | 4 | Accept | 0.2414 | 1.2954 | 0.2411 | 0.24441 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0048396 | | | | | | | | | | KernelScale: 0.02413 |
| 122 | 4 | Accept | 0.5157 | 9.6657 | 0.2411 | 0.24441 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 0.071659 | | | | | | | | | | Lambda: 1.3447e-05 |
| 123 | 4 | Accept | 0.53186 | 390.32 | 0.2402 | 0.24441 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 416.46 | | | | | | | | | | KernelScale: 0.0019087 | | 124 | 4 | Best | 0.2402 | 1.4571 | 0.2402 | 0.24441 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.033468 | | | | | | | | | | KernelScale: 0.073489 |
| 125 | 4 | Accept | 0.2402 | 1.3713 | 0.2402 | 0.24441 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0040249 | | | | | | | | | | KernelScale: 0.030373 |
| 126 | 3 | Accept | 0.24529 | 39.503 | 0.2402 | 0.24441 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.32116 | | | | | | | | | | KernelScale: 0.0076281 | | 127 | 3 | Accept | 0.25576 | 1.4031 | 0.2402 | 0.24441 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.018022 | | | | | | | | | | KernelScale: 0.3703 |
| 128 | 6 | Accept | 0.2426 | 1.3555 | 0.2402 | 0.24229 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.029999 | | | | | | | | | | KernelScale: 0.046245 |
| 129 | 3 | Accept | 0.24499 | 9.5236 | 0.2402 | 0.24419 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.38346 | | | | | | | | | | KernelScale: 0.01786 | | 130 | 3 | Accept | 0.28059 | 0.12828 | 0.2402 | 0.24419 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN | |===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 131 | 3 | Accept | 0.6886 | 1.4213 | 0.2402 | 0.24419 | linear | Coding: onevsone | | | | | | | | | | Lambda: 3.0763 | | | | | | | | | | Learner: logistic | | 132 | 3 | Accept | 0.4487 | 3.111 | 0.2402 | 0.24419 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 4.845 | | | | | | | | | | KernelScale: 0.74028 |
| 133 | 6 | Accept | 0.24469 | 8.3042 | 0.2402 | 0.24441 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.45335 | | | | | | | | | | KernelScale: 0.02034 |
| 134 | 3 | Accept | 0.26204 | 1.7109 | 0.2402 | 0.24441 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0011217 | | | | | | | | | | KernelScale: 0.10651 | | 135 | 3 | Accept | 0.74185 | 4.0388 | 0.2402 | 0.24441 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 10.518 | | | | | | | | | | Lambda: 0.20458 | | 136 | 3 | Accept | 0.29734 | 9.9598 | 0.2402 | 0.24441 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 107 | | | | | | | | | | LearnRate: 0.0023672 | | | | | | | | | | MinLeafSize: 197 | | 137 | 3 | Accept | 0.44391 | 2.0399 | 0.2402 | 0.24441 | nb | DistributionNames: kernel | | | | | | | | | | Width: 0.00025608 |
| 138 | 6 | Accept | 0.24559 | 3.2102 | 0.2402 | 0.24276 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.5211 | | | | | | | | | | KernelScale: 0.04163 |
| 139 | 4 | Accept | 0.25067 | 1.4995 | 0.2402 | 0.24276 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.030306 | | | | | | | | | | KernelScale: 0.37391 | | 140 | 4 | Accept | 0.24559 | 1.6227 | 0.2402 | 0.24276 | linear | Coding: onevsone | | | | | | | | | | Lambda: 3.0973e-07 | | | | | | | | | | Learner: svm | |===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 141 | 4 | Accept | 0.74634 | 7.0945 | 0.2402 | 0.24276 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 0.01094 | | | | | | | | | | Lambda: 0.0013866 |
| 142 | 4 | Accept | 0.29076 | 1.3294 | 0.2402 | 0.24334 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.006875 | | | | | | | | | | KernelScale: 0.38629 |
| 143 | 4 | Accept | 0.24379 | 1.4595 | 0.2402 | 0.24269 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0010315 | | | | | | | | | | KernelScale: 0.0079737 |
| 144 | 4 | Accept | 0.24499 | 4.9134 | 0.2402 | 0.24226 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.62692 | | | | | | | | | | KernelScale: 0.033311 |
| 145 | 4 | Accept | 0.24469 | 12.208 | 0.2402 | 0.24242 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0010122 | | | | | | | | | | KernelScale: 0.0010167 |
| 146 | 4 | Accept | 0.38588 | 1.5037 | 0.2402 | 0.24388 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0011127 | | | | | | | | | | KernelScale: 0.52838 |
| 147 | 4 | Accept | 0.24589 | 1.3026 | 0.2402 | 0.24228 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.1424 | | | | | | | | | | KernelScale: 0.58035 |
| 148 | 4 | Accept | 0.2408 | 1.2582 | 0.2402 | 0.24165 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0010168 | | | | | | | | | | KernelScale: 0.032668 |
| 149 | 4 | Accept | 0.24469 | 1.2764 | 0.2402 | 0.24197 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.13008 | | | | | | | | | | KernelScale: 0.53455 |
| 150 | 5 | Accept | 0.24469 | 6.7111 | 0.2402 | 0.24222 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.45516 | | | | | | | | | | KernelScale: 0.024796 |
|===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 151 | 5 | Accept | 0.30422 | 1.5471 | 0.2402 | 0.24244 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.36805 | | | | | | | | | | KernelScale: 4.7124 |
| 152 | 6 | Accept | 0.24559 | 1.5701 | 0.2402 | 0.2414 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.21039 | | | | | | | | | | KernelScale: 0.63504 |
| 153 | 5 | Accept | 0.24529 | 66.311 | 0.2402 | 0.24243 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.45714 | | | | | | | | | | KernelScale: 0.0069546 | | 154 | 5 | Accept | 0.24589 | 1.604 | 0.2402 | 0.24243 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0010712 | | | | | | | | | | KernelScale: 0.043866 |
| 155 | 5 | Accept | 0.29345 | 1.4823 | 0.2402 | 0.24262 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.032053 | | | | | | | | | | KernelScale: 0.88175 |
| 156 | 6 | Accept | 0.25247 | 1.4792 | 0.2402 | 0.24289 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.14103 | | | | | | | | | | KernelScale: 0.91489 |
| 157 | 6 | Accept | 0.2405 | 1.5732 | 0.2402 | 0.242 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0010011 | | | | | | | | | | KernelScale: 0.012195 |
| 158 | 6 | Accept | 0.2426 | 1.5232 | 0.2402 | 0.24223 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.52301 | | | | | | | | | | KernelScale: 0.69511 |
| 159 | 6 | Accept | 0.25456 | 1.9467 | 0.2402 | 0.24253 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.63305 | | | | | | | | | | KernelScale: 2.1073 |
| 160 | 5 | Accept | 0.24559 | 133.37 | 0.2402 | 0.24253 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.73914 | | | | | | | | | | KernelScale: 0.005832 | |===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 161 | 5 | Accept | 0.57314 | 8.1723 | 0.2402 | 0.24253 | kernel | Coding: onevsall | | | | | | | | | | KernelScale: 0.071267 | | | | | | | | | | Lambda: 1.4009e-06 |
| 162 | 5 | Accept | 0.24649 | 1.6878 | 0.2402 | 0.24325 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.19825 | | | | | | | | | | KernelScale: 0.74742 |
| 163 | 5 | Accept | 0.2417 | 1.5824 | 0.2402 | 0.24297 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0010619 | | | | | | | | | | KernelScale: 0.01008 |
| 164 | 4 | Accept | 0.45528 | 140.46 | 0.2402 | 0.24441 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.00706 | | | | | | | | | | KernelScale: 0.030909 | | 165 | 4 | Accept | 0.31409 | 1.5573 | 0.2402 | 0.24441 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.56217 | | | | | | | | | | KernelScale: 7.5961 |
| 166 | 4 | Accept | 0.46575 | 2.2317 | 0.2402 | 0.24441 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.10607 | | | | | | | | | | KernelScale: 0.67446 |
| 167 | 4 | Accept | 0.24529 | 3.3215 | 0.2402 | 0.24334 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 1.4843 | | | | | | | | | | KernelScale: 0.069679 |
| 168 | 4 | Accept | 0.25396 | 1.3297 | 0.2402 | 0.24353 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.52671 | | | | | | | | | | KernelScale: 1.9394 |
| 169 | 5 | Accept | 0.2402 | 1.5257 | 0.2402 | 0.24313 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0010378 | | | | | | | | | | KernelScale: 0.012749 |
| 170 | 6 | Accept | 0.3464 | 1.8583 | 0.2402 | 0.24237 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.50591 | | | | | | | | | | KernelScale: 9.2835 |
|===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 171 | 6 | Accept | 0.24499 | 12.298 | 0.2402 | 0.24203 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0018442 | | | | | | | | | | KernelScale: 0.0012506 |
| 172 | 6 | Accept | 0.30272 | 1.6031 | 0.2402 | 0.24186 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 2.8265 | | | | | | | | | | KernelScale: 11.897 |
| 173 | 6 | Accept | 0.24349 | 1.4247 | 0.2402 | 0.24134 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0010459 | | | | | | | | | | KernelScale: 0.036404 |
| 174 | 6 | Accept | 0.29794 | 1.6502 | 0.2402 | 0.24299 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 5.4893 | | | | | | | | | | KernelScale: 13.359 |
| 175 | 5 | Accept | 0.24529 | 109.43 | 0.2402 | 0.24345 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.88969 | | | | | | | | | | KernelScale: 0.0075436 | | 176 | 5 | Accept | 0.25007 | 1.6406 | 0.2402 | 0.24345 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0010212 | | | | | | | | | | KernelScale: 0.071763 |
| 177 | 5 | Accept | 0.24619 | 2.171 | 0.2402 | 0.24259 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0010341 | | | | | | | | | | KernelScale: 0.0039146 |
| 178 | 5 | Accept | 0.32576 | 1.6841 | 0.2402 | 0.24185 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 1.6102 | | | | | | | | | | KernelScale: 14.532 |
| 179 | 5 | Accept | 0.8262 | 890.49 | 0.2402 | 0.24179 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.18649 | | | | | | | | | | KernelScale: 0.0010802 |
| 180 | 5 | Accept | 0.30212 | 1.5671 | 0.2402 | 0.24174 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 6.0833 | | | | | | | | | | KernelScale: 15.913 |
|===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 181 | 5 | Accept | 0.24499 | 14.697 | 0.2402 | 0.24192 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0078062 | | | | | | | | | | KernelScale: 0.0020664 |
| 182 | 5 | Accept | 0.24529 | 61.944 | 0.2402 | 0.2414 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 1.0466 | | | | | | | | | | KernelScale: 0.011265 |
| 183 | 5 | Accept | 0.24499 | 17.644 | 0.2402 | 0.24186 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0021279 | | | | | | | | | | KernelScale: 0.0010743 |
| 184 | 5 | Accept | 0.29794 | 1.6007 | 0.2402 | 0.24189 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0010132 | | | | | | | | | | KernelScale: 0.17669 |
| 185 | 4 | Accept | 0.24918 | 279.94 | 0.2402 | 0.24236 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 891.39 | | | | | | | | | | KernelScale: 0.076868 | | 186 | 4 | Accept | 0.26593 | 1.4006 | 0.2402 | 0.24236 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.14381 | | | | | | | | | | KernelScale: 1.2859 |
| 187 | 4 | Accept | 0.24768 | 1.438 | 0.2402 | 0.24161 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.47763 | | | | | | | | | | KernelScale: 1.4081 |
| 188 | 4 | Accept | 0.24589 | 2.5142 | 0.2402 | 0.2416 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0010362 | | | | | | | | | | KernelScale: 0.0024579 |
| 189 | 4 | Accept | 0.2405 | 1.2662 | 0.2402 | 0.24215 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 11.232 | | | | | | | | | | KernelScale: 1.4768 |
| 190 | 4 | Accept | 0.24678 | 1.2604 | 0.2402 | 0.24174 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.48152 | | | | | | | | | | KernelScale: 1.1771 |
|===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 191 | 4 | Accept | 0.25426 | 1.2846 | 0.2402 | 0.24186 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.47194 | | | | | | | | | | KernelScale: 1.7714 |
| 192 | 5 | Accept | 0.24768 | 1.3295 | 0.2402 | 0.24188 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.44397 | | | | | | | | | | KernelScale: 1.3493 |
| 193 | 6 | Accept | 0.2414 | 1.3979 | 0.2402 | 0.24154 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 1.8796 | | | | | | | | | | KernelScale: 1.4211 |
| 194 | 6 | Accept | 0.24499 | 18.984 | 0.2402 | 0.24225 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0018335 | | | | | | | | | | KernelScale: 0.0010104 |
| 195 | 6 | Accept | 0.25037 | 1.5112 | 0.2402 | 0.24212 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.41413 | | | | | | | | | | KernelScale: 1.4211 |
| 196 | 6 | Accept | 0.24499 | 6.007 | 0.2402 | 0.24191 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0010001 | | | | | | | | | | KernelScale: 0.0013198 |
| 197 | 6 | Accept | 0.24529 | 111.16 | 0.2402 | 0.24215 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.046722 | | | | | | | | | | KernelScale: 0.0016388 |
| 198 | 5 | Accept | 0.24499 | 18.688 | 0.2402 | 0.24186 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0033281 | | | | | | | | | | KernelScale: 0.0012057 | | 199 | 5 | Accept | 0.2426 | 1.4789 | 0.2402 | 0.24186 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 3.0066 | | | | | | | | | | KernelScale: 1.3889 |
| 200 | 6 | Accept | 0.2417 | 1.499 | 0.2402 | 0.24212 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 1.7327 | | | | | | | | | | KernelScale: 1.3637 |
|===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 201 | 5 | Accept | 0.26473 | 277.53 | 0.2402 | 0.24176 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.19781 | | | | | | | | | | KernelScale: 0.0010905 | | 202 | 5 | Accept | 0.30332 | 1.5632 | 0.2402 | 0.24176 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.075228 | | | | | | | | | | KernelScale: 1.8677 |
| 203 | 5 | Accept | 0.3108 | 1.5519 | 0.2402 | 0.24188 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 2.8086 | | | | | | | | | | KernelScale: 16.078 |
| 204 | 5 | Accept | 0.2414 | 1.4413 | 0.2402 | 0.24147 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 3.1547 | | | | | | | | | | KernelScale: 0.57043 |
| 205 | 5 | Accept | 0.3108 | 1.5576 | 0.2402 | 0.24195 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 3.0235 | | | | | | | | | | KernelScale: 16.787 |
| 206 | 5 | Accept | 0.2411 | 1.3939 | 0.2402 | 0.24163 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0010447 | | | | | | | | | | KernelScale: 0.022855 |
| 207 | 5 | Accept | 0.29704 | 1.5568 | 0.2402 | 0.24155 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.11574 | | | | | | | | | | KernelScale: 1.8573 |
| 208 | 5 | Accept | 0.2426 | 1.4114 | 0.2402 | 0.24195 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 2.2749 | | | | | | | | | | KernelScale: 1.2395 |
| 209 | 5 | Accept | 0.24559 | 3.445 | 0.2402 | 0.2417 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 560.48 | | | | | | | | | | KernelScale: 1.4181 |
| 210 | 4 | Accept | 0.24678 | 266.64 | 0.2402 | 0.24136 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 11.849 | | | | | | | | | | KernelScale: 0.0095181 | |===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 211 | 4 | Accept | 0.24589 | 2.1278 | 0.2402 | 0.24136 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 41.82 | | | | | | | | | | KernelScale: 0.71147 |
| 212 | 3 | Accept | 0.95034 | 1042.9 | 0.2402 | 0.24153 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 10.672 | | | | | | | | | | KernelScale: 0.0013453 | | 213 | 3 | Accept | 0.24649 | 2.0006 | 0.2402 | 0.24153 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 210.22 | | | | | | | | | | KernelScale: 1.4654 |
| 214 | 6 | Accept | 0.26653 | 1.2521 | 0.2402 | 0.24136 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.16417 | | | | | | | | | | KernelScale: 1.3626 |
| 215 | 4 | Accept | 0.2429 | 1.5969 | 0.2402 | 0.24136 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0010295 | | | | | | | | | | KernelScale: 0.008743 | | 216 | 4 | Accept | 0.33353 | 1.2604 | 0.2402 | 0.24136 | nb | DistributionNames: kernel | | | | | | | | | | Width: 0.0017992 | | 217 | 4 | Accept | 0.28059 | 0.2661 | 0.2402 | 0.24136 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |
| 218 | 4 | Accept | 0.24798 | 1.3024 | 0.2402 | 0.24173 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 66.132 | | | | | | | | | | KernelScale: 16.829 |
| 219 | 4 | Accept | 0.24529 | 1.255 | 0.2402 | 0.24156 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 1.0858 | | | | | | | | | | KernelScale: 1.3599 |
| 220 | 4 | Accept | 0.2408 | 1.2539 | 0.2402 | 0.24183 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 3.8001 | | | | | | | | | | KernelScale: 1.3322 |
|===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 221 | 4 | Accept | 0.24589 | 1.8906 | 0.2402 | 0.24185 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 399.07 | | | | | | | | | | KernelScale: 2.1791 |
| 222 | 4 | Accept | 0.2414 | 1.2821 | 0.2402 | 0.24187 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 4.8589 | | | | | | | | | | KernelScale: 1.3542 |
| 223 | 4 | Accept | 0.2423 | 1.2804 | 0.2402 | 0.24131 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 2.6082 | | | | | | | | | | KernelScale: 1.3565 |
| 224 | 4 | Accept | 0.24559 | 3.1158 | 0.2402 | 0.24116 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 536.1 | | | | | | | | | | KernelScale: 1.4679 |
| 225 | 4 | Accept | 0.25067 | 1.3228 | 0.2402 | 0.24178 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 74.127 | | | | | | | | | | KernelScale: 18.528 |
| 226 | 3 | Accept | 0.28358 | 328 | 0.2402 | 0.24207 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.47384 | | | | | | | | | | KernelScale: 0.001006 | | 227 | 3 | Accept | 0.24499 | 6.2167 | 0.2402 | 0.24207 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0026293 | | | | | | | | | | KernelScale: 0.0019141 |
| 228 | 6 | Accept | 0.2402 | 1.2501 | 0.2402 | 0.24173 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0010356 | | | | | | | | | | KernelScale: 0.012867 |
| 229 | 3 | Accept | 0.24589 | 1.4248 | 0.2402 | 0.24173 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 104.21 | | | | | | | | | | KernelScale: 17.45 | | 230 | 3 | Accept | 0.43015 | 0.13765 | 0.2402 | 0.24173 | discr | Delta: 0.00951 | | | | | | | | | | Gamma: 0.68613 | |===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 231 | 3 | Accept | 0.47383 | 2.5354 | 0.2402 | 0.24173 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 1.2431 | | | | | | | | | | KernelScale: 0.76632 | | 232 | 3 | Accept | 0.67664 | 0.30377 | 0.2402 | 0.24173 | knn | NumNeighbors: 8 | | | | | | | | | | Distance: jaccard |
| 233 | 6 | Accept | 0.2414 | 1.2369 | 0.2402 | 0.24133 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 820.98 | | | | | | | | | | KernelScale: 17.331 |
| 234 | 3 | Accept | 0.27759 | 1.4452 | 0.2402 | 0.24133 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 22.198 | | | | | | | | | | KernelScale: 17.97 | | 235 | 3 | Accept | 0.28059 | 0.13623 | 0.2402 | 0.24133 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN | | 236 | 3 | Accept | 0.52408 | 1.503 | 0.2402 | 0.24133 | knn | NumNeighbors: 351 | | | | | | | | | | Distance: mahalanobis | | 237 | 3 | Accept | 0.27789 | 5.8867 | 0.2402 | 0.24133 | ensemble | Method: RUSBoost | | | | | | | | | | NumLearningCycles: 68 | | | | | | | | | | LearnRate: 0.18331 | | | | | | | | | | MinLeafSize: 3 |
| 238 | 6 | Accept | 0.24499 | 1.2819 | 0.2402 | 0.24188 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 201.86 | | | | | | | | | | KernelScale: 18.062 |
| 239 | 3 | Accept | 0.25097 | 1.4345 | 0.2402 | 0.24116 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 105.8 | | | | | | | | | | KernelScale: 23.315 | | 240 | 3 | Accept | 0.30212 | 0.12275 | 0.2402 | 0.24116 | tree | MinLeafSize: 122 | |===========================================================================================================================================| | Iter | Active | Eval | Validation | Time for training | Observed min | Estimated min | Learner | Hyperparameter: Value | | | workers | result | loss | & validation (sec)| validation loss | validation loss | | | |===========================================================================================================================================| | 241 | 3 | Accept | 0.25307 | 11.978 | 0.2402 | 0.24116 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 119 | | | | | | | | | | LearnRate: 0.60308 | | | | | | | | | | MinLeafSize: 1 | | 242 | 3 | Accept | 0.44182 | 11.435 | 0.2402 | 0.24116 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 6.2783 | | | | | | | | | | KernelScale: 0.19364 |
__________________________________________________________ Optimization completed. Total iterations: 242 Total elapsed time: 1907.7403 seconds Total time for training and validation: 4936.5339 seconds Best observed learner is a multiclass svm model with: Coding (ECOC): onevsone BoxConstraint: 0.033468 KernelScale: 0.073489 Observed validation loss: 0.2402 Time for training and validation: 1.4571 seconds Best estimated learner (returned model) is a multiclass svm model with: Coding (ECOC): onevsone BoxConstraint: 0.0010378 KernelScale: 0.012749 Estimated validation loss: 0.24116 Estimated time for training and validation: 1.5595 seconds Documentation for fitcauto display
Последняя модель, возвращенная fitcauto соответствует лучшему предполагаемому ученику. Перед возвращением модели функция переобучает её, используя все данные обучения (creditTrain), перечисленные Learner тип (или модель) и отображаемые значения гиперпараметров.
Оценка производительности тестового набора
Модель Mdl соответствует лучшей точке байесовской оптимизации согласно 'min-visited-mean' критерий. Чтобы оценить, как модель будет работать с новыми данными, посмотрите на наблюдаемую точность перекрестной проверки модели (cvAccuracy) и его общая оценочная производительность на основе байесовской оптимизации (estimatedAccuracy).
[x,~,iteration] = bestPoint(Results,'Criterion','min-visited-mean'); cvError = Results.ObjectiveTrace(iteration); cvAccuracy = 1 - cvError
cvAccuracy = 0.7598
estimatedError = predictObjective(Results,x); estimatedAccuracy = 1 - estimatedError
estimatedAccuracy = 0.7588
Оцените производительность модели в тестовом наборе. Создайте матрицу путаницы из результатов и укажите порядок классов в матрице путаницы.
testAccuracy = 1 - loss(Mdl,creditTest,'Rating')testAccuracy = 0.7438
cm = confusionchart(creditTest.Rating,predict(Mdl,creditTest));
sortClasses(cm,{'AAA','AA','A','BBB','BB','B','CCC'})
В зависимости от размера ваших данных и указанного количества учащихся, fitcauto может занять некоторое время, чтобы бежать. При наличии лицензии Parallel Computing Toolbox можно ускорить вычисления путем параллельного выполнения оптимизации. Для этого укажите 'HyperparameterOptimizationOptions',struct('UseParallel',true). Для управления другими аспектами оптимизации в структуру можно включить другие поля. Посмотрите HyperparameterOptimizationOptions.
Если вы не уверены, какие модели лучше всего подходят для вашего набора данных, можно также использовать приложение Classification Learner. Используя приложение, вы можете выполнить гиперпараметрическую настройку для различных моделей и выбрать оптимизированную модель, которая работает лучше всего. Хотя перед настройкой гиперпараметров модели необходимо выбрать определенную модель, программа Classification Learner обеспечивает большую гибкость при выборе оптимизируемых гиперпараметров и установке значений гиперпараметров. Однако параллельно оптимизировать нельзя, выберите 'linear' или 'kernel' учащиеся, укажите веса наблюдений или укажите предыдущие вероятности в приложении. Дополнительные сведения см. в разделе Оптимизация гиперпараметров в приложении Classification Learner App.
Если известно, какие модели могут соответствовать данным, можно также использовать соответствующие функции аппроксимации модели и указать 'OptimizeHyperparameters' аргумент пары имя-значение для настройки гиперпараметров. Можно сравнить результаты по моделям, чтобы выбрать лучший классификатор. Пример этого процесса см. в разделе Переход к автоматизации выбора модели с использованием байесовской оптимизации.
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