fitcauto
Автоматически выберите модель классификации оптимизированными гиперпараметрами
Синтаксис
Описание
Учитывая предиктор и данные об ответе, fitcauto автоматически пробует выбор типов модели классификации с различными значениями гиперпараметра. Функция использует Байесовую оптимизацию, чтобы выбрать модели и их значения гиперпараметра, и вычисляет ошибку классификации перекрестных проверок для каждой модели. После того, как оптимизация завершена, fitcauto возвращает модель, обученную на целом наборе данных, который, как ожидают, лучше всего классифицирует новые данные. Можно использовать predict и loss объектные функции возвращенной модели, чтобы классифицировать новые данные и вычислить ошибку классификации наборов тестов, соответственно.
Используйте fitcauto когда вы не уверены, который вводит классификатор, лучше всего удовлетворяют вашим данным. Для получения информации об альтернативных методах для настройки гиперпараметров моделей классификации см. альтернативную функциональность.
Mdl = fitcauto(Tbl,ResponseVarName)Mdl классификации настроенными гиперпараметрами. Таблица Tbl содержит переменные предикторы и переменную отклика, где ResponseVarName имя переменной отклика.
Mdl = fitcauto(___,Name,Value)HyperparameterOptimizationOptions аргумент пары "имя-значение", чтобы задать, как Байесова оптимизация выполняется.
[ дополнительно возвращает Mdl,OptimizationResults] = fitcauto(___)OptimizationResults, BayesianOptimization объект, содержащий результаты выбора модели и настраивающего процесса гиперпараметра.
Примеры
Автоматически выберите классификатор Используя табличные данные
Используйте fitcauto автоматически выбрать модель классификации оптимизированными гиперпараметрами, учитывая предиктор и данные об ответе, хранимые в таблице.
Загрузка данных
Загрузите carbig набор данных, который содержит измерения автомобилей, сделанных в 1970-х и в начале 1980-х.
load carbigКатегоризируйте автомобили на основе того, были ли они сделаны в США.
Origin = categorical(cellstr(Origin));
Origin = mergecats(Origin,{'France','Japan','Germany', ...
'Sweden','Italy','England'},'NotUSA');Составьте таблицу, содержащую переменные предикторы Acceleration, Displacement, и так далее, а также переменная отклика Origin.
cars = table(Acceleration,Displacement,Horsepower, ...
Model_Year,MPG,Weight,Origin);Данные о разделе
Разделите данные в наборы обучающих данных и наборы тестов. Используйте приблизительно 80% наблюдений для выбора модели и настраивающего процесса гиперпараметра, и 20% наблюдений, чтобы проверить производительность итоговой модели, возвращенной fitcauto. Используйте cvpartition разделить данные.
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,:);
Запустите fitcauto
Передайте обучающие данные fitcauto. По умолчанию, fitcauto определяет соответствующие типы модели, чтобы попробовать, использует Байесовую оптимизацию, чтобы найти хорошие гиперзначения параметров и возвращает обученную модель Mdl с лучшей ожидаемой производительностью.
Ожидайте, что этот процесс займет время. Чтобы ускорить процесс оптимизации, полагайте, что определение запускает оптимизацию параллельно, если у вас есть лицензия Parallel Computing Toolbox™. Для этого передайте 'HyperparameterOptimizationOptions',struct('UseParallel',true) к fitcauto как аргумент 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.
|====================================================================================================================| | Iter | Eval | Objective | Objective | BestSoFar | BestSoFar | Learner | Hyperparameter: Value | | | result | | runtime | (observed) | (estim.) | | | |====================================================================================================================| | 1 | Best | 0.12923 | 12.533 | 0.12923 | 0.12923 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 201 | | | | | | | | | MinLeafSize: 7 |
| 2 | Accept | 0.18269 | 0.6441 | 0.12923 | 0.12923 | knn | NumNeighbors: 3 |
| 3 | Accept | 0.23397 | 0.12905 | 0.12923 | 0.20782 | knn | NumNeighbors: 91 |
| 4 | Accept | 0.16308 | 12.228 | 0.12923 | 0.14852 | ensemble | Method: LogitBoost | | | | | | | | | NumLearningCycles: 274 | | | | | | | | | MinLeafSize: 15 |
| 5 | Accept | 0.20833 | 0.13612 | 0.12923 | 0.14852 | knn | NumNeighbors: 4 |
| 6 | Accept | 0.22115 | 0.13461 | 0.12923 | 0.14852 | knn | NumNeighbors: 28 |
| 7 | Accept | 0.16923 | 0.25707 | 0.12923 | 0.14852 | tree | MinLeafSize: 105 |
| 8 | Accept | 0.37179 | 0.64601 | 0.12923 | 0.14852 | svm | BoxConstraint: 0.022186 | | | | | | | | | KernelScale: 0.085527 |
| 9 | Accept | 0.37179 | 0.12828 | 0.12923 | 0.14852 | svm | BoxConstraint: 0.045899 | | | | | | | | | KernelScale: 0.0024758 |
| 10 | Accept | 0.24615 | 0.99945 | 0.12923 | 0.14852 | nb | DistributionNames: kernel | | | | | | | | | Width: 1.1327 |
| 11 | Accept | 0.16923 | 0.098106 | 0.12923 | 0.14852 | tree | MinLeafSize: 78 |
| 12 | Accept | 0.26923 | 0.11213 | 0.12923 | 0.14852 | svm | BoxConstraint: 11.063 | | | | | | | | | KernelScale: 15.114 |
| 13 | Best | 0.12615 | 0.10159 | 0.12615 | 0.14852 | tree | MinLeafSize: 3 |
| 14 | Accept | 0.21154 | 0.096928 | 0.12615 | 0.14852 | knn | NumNeighbors: 2 |
| 15 | Accept | 0.13538 | 0.10336 | 0.12615 | 0.15014 | tree | MinLeafSize: 1 |
| 16 | Accept | 0.13538 | 0.096076 | 0.12615 | 0.1482 | tree | MinLeafSize: 2 |
| 17 | Best | 0.12308 | 10.115 | 0.12308 | 0.13678 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 208 | | | | | | | | | MinLeafSize: 10 |
| 18 | Accept | 0.37179 | 0.13789 | 0.12308 | 0.13678 | svm | BoxConstraint: 116.46 | | | | | | | | | KernelScale: 0.52908 |
| 19 | Accept | 0.22769 | 0.16645 | 0.12308 | 0.13678 | nb | DistributionNames: normal | | | | | | | | | Width: NaN |
| 20 | Accept | 0.22115 | 0.087468 | 0.12308 | 0.13678 | knn | NumNeighbors: 8 |
|====================================================================================================================| | Iter | Eval | Objective | Objective | BestSoFar | BestSoFar | Learner | Hyperparameter: Value | | | result | | runtime | (observed) | (estim.) | | | |====================================================================================================================| | 21 | Accept | 0.37179 | 0.13455 | 0.12308 | 0.13678 | svm | BoxConstraint: 45.341 | | | | | | | | | KernelScale: 0.76949 |
| 22 | Accept | 0.12615 | 0.10641 | 0.12308 | 0.13678 | tree | MinLeafSize: 3 |
| 23 | Best | 0.10769 | 0.075704 | 0.10769 | 0.13678 | tree | MinLeafSize: 5 |
| 24 | Accept | 0.22769 | 0.28513 | 0.10769 | 0.13678 | nb | DistributionNames: kernel | | | | | | | | | Width: 0.42571 |
| 25 | Accept | 0.12615 | 0.072307 | 0.10769 | 0.13846 | tree | MinLeafSize: 11 |
| 26 | Accept | 0.13782 | 0.084552 | 0.10769 | 0.13846 | svm | BoxConstraint: 9.7286 | | | | | | | | | KernelScale: 293.41 |
| 27 | Accept | 0.22769 | 0.098663 | 0.10769 | 0.13846 | nb | DistributionNames: normal | | | | | | | | | Width: NaN |
| 28 | Accept | 0.21795 | 0.087931 | 0.10769 | 0.13846 | knn | NumNeighbors: 42 |
| 29 | Accept | 0.24308 | 0.26915 | 0.10769 | 0.13846 | nb | DistributionNames: kernel | | | | | | | | | Width: 4.4662 |
| 30 | Accept | 0.16308 | 11.221 | 0.10769 | 0.13846 | ensemble | Method: LogitBoost | | | | | | | | | NumLearningCycles: 267 | | | | | | | | | MinLeafSize: 131 |
| 31 | Accept | 0.24308 | 0.26485 | 0.10769 | 0.13846 | nb | DistributionNames: kernel | | | | | | | | | Width: 0.66296 |
| 32 | Accept | 0.22115 | 0.067784 | 0.10769 | 0.13846 | knn | NumNeighbors: 28 |
| 33 | Accept | 0.13846 | 0.11902 | 0.10769 | 0.13714 | tree | MinLeafSize: 25 |
| 34 | Accept | 0.21474 | 0.07273 | 0.10769 | 0.13714 | knn | NumNeighbors: 14 |
| 35 | Accept | 0.16615 | 9.2855 | 0.10769 | 0.13714 | ensemble | Method: LogitBoost | | | | | | | | | NumLearningCycles: 215 | | | | | | | | | MinLeafSize: 13 |
| 36 | Accept | 0.15077 | 11.786 | 0.10769 | 0.13714 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 254 | | | | | | | | | MinLeafSize: 31 |
| 37 | Accept | 0.22769 | 0.081882 | 0.10769 | 0.13714 | nb | DistributionNames: normal | | | | | | | | | Width: NaN |
| 38 | Accept | 0.37179 | 0.07709 | 0.10769 | 0.13714 | svm | BoxConstraint: 0.0073633 | | | | | | | | | KernelScale: 774.33 |
| 39 | Accept | 0.16923 | 0.077139 | 0.10769 | 0.1325 | tree | MinLeafSize: 82 |
| 40 | Accept | 0.20833 | 0.087004 | 0.10769 | 0.1325 | knn | NumNeighbors: 4 |
|====================================================================================================================| | Iter | Eval | Objective | Objective | BestSoFar | BestSoFar | Learner | Hyperparameter: Value | | | result | | runtime | (observed) | (estim.) | | | |====================================================================================================================| | 41 | Accept | 0.16308 | 12.164 | 0.10769 | 0.1325 | ensemble | Method: LogitBoost | | | | | | | | | NumLearningCycles: 274 | | | | | | | | | MinLeafSize: 150 |
| 42 | Accept | 0.22462 | 0.28662 | 0.10769 | 0.1325 | nb | DistributionNames: kernel | | | | | | | | | Width: 121.64 |
| 43 | Accept | 0.17846 | 10.427 | 0.10769 | 0.1325 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 229 | | | | | | | | | MinLeafSize: 117 |
| 44 | Accept | 0.16923 | 0.088318 | 0.10769 | 0.12874 | tree | MinLeafSize: 84 |
| 45 | Accept | 0.22769 | 0.079169 | 0.10769 | 0.12874 | nb | DistributionNames: normal | | | | | | | | | Width: NaN |
| 46 | Accept | 0.22769 | 0.065898 | 0.10769 | 0.12874 | nb | DistributionNames: normal | | | | | | | | | Width: NaN |
| 47 | Accept | 0.16615 | 9.3444 | 0.10769 | 0.12874 | ensemble | Method: LogitBoost | | | | | | | | | NumLearningCycles: 212 | | | | | | | | | MinLeafSize: 49 |
| 48 | Accept | 0.14154 | 13.721 | 0.10769 | 0.12874 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 288 | | | | | | | | | MinLeafSize: 25 |
| 49 | Accept | 0.23077 | 0.2725 | 0.10769 | 0.12874 | nb | DistributionNames: kernel | | | | | | | | | Width: 73.249 |
| 50 | Accept | 0.37179 | 0.11629 | 0.10769 | 0.12874 | svm | BoxConstraint: 0.0036501 | | | | | | | | | KernelScale: 1.0504 |
| 51 | Accept | 0.21474 | 0.14443 | 0.10769 | 0.12874 | svm | BoxConstraint: 64.859 | | | | | | | | | KernelScale: 23.779 |
| 52 | Accept | 0.37179 | 0.11504 | 0.10769 | 0.12874 | svm | BoxConstraint: 0.16622 | | | | | | | | | KernelScale: 4.4901 |
| 53 | Accept | 0.25846 | 0.26577 | 0.10769 | 0.12874 | nb | DistributionNames: kernel | | | | | | | | | Width: 0.079498 |
| 54 | Accept | 0.21154 | 0.07558 | 0.10769 | 0.12874 | knn | NumNeighbors: 2 |
| 55 | Accept | 0.12308 | 11.525 | 0.10769 | 0.12874 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 234 | | | | | | | | | MinLeafSize: 8 |
| 56 | Accept | 0.36538 | 0.11498 | 0.10769 | 0.12874 | svm | BoxConstraint: 271.6 | | | | | | | | | KernelScale: 2.743 |
| 57 | Accept | 0.16615 | 9.9644 | 0.10769 | 0.12874 | ensemble | Method: LogitBoost | | | | | | | | | NumLearningCycles: 248 | | | | | | | | | MinLeafSize: 117 |
| 58 | Accept | 0.37179 | 0.10707 | 0.10769 | 0.12874 | svm | BoxConstraint: 7.5785 | | | | | | | | | KernelScale: 0.0066815 |
| 59 | Accept | 0.37179 | 0.10387 | 0.10769 | 0.12874 | svm | BoxConstraint: 0.0017765 | | | | | | | | | KernelScale: 0.86786 |
| 60 | Accept | 0.37179 | 0.11451 | 0.10769 | 0.12874 | svm | BoxConstraint: 0.011465 | | | | | | | | | KernelScale: 0.02747 |
|====================================================================================================================| | Iter | Eval | Objective | Objective | BestSoFar | BestSoFar | Learner | Hyperparameter: Value | | | result | | runtime | (observed) | (estim.) | | | |====================================================================================================================| | 61 | Accept | 0.12308 | 0.075995 | 0.10769 | 0.12862 | tree | MinLeafSize: 12 |
| 62 | Accept | 0.29167 | 0.12678 | 0.10769 | 0.12862 | svm | BoxConstraint: 11.939 | | | | | | | | | KernelScale: 11.002 |
| 63 | Accept | 0.21795 | 0.085439 | 0.10769 | 0.12862 | knn | NumNeighbors: 6 |
| 64 | Accept | 0.18269 | 0.075299 | 0.10769 | 0.12862 | knn | NumNeighbors: 3 |
| 65 | Accept | 0.12615 | 0.09094 | 0.10769 | 0.1273 | tree | MinLeafSize: 3 |
| 66 | Accept | 0.16923 | 0.074034 | 0.10769 | 0.12662 | tree | MinLeafSize: 56 |
| 67 | Accept | 0.1891 | 0.068062 | 0.10769 | 0.12662 | knn | NumNeighbors: 1 |
| 68 | Accept | 0.11692 | 12.685 | 0.10769 | 0.12662 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 270 | | | | | | | | | MinLeafSize: 4 |
| 69 | Accept | 0.22769 | 0.08331 | 0.10769 | 0.12662 | nb | DistributionNames: normal | | | | | | | | | Width: NaN |
| 70 | Accept | 0.37231 | 0.23243 | 0.10769 | 0.12662 | nb | DistributionNames: kernel | | | | | | | | | Width: 1629.5 |
| 71 | Accept | 0.16923 | 0.07617 | 0.10769 | 0.12684 | tree | MinLeafSize: 61 |
| 72 | Accept | 0.22769 | 0.076927 | 0.10769 | 0.12684 | nb | DistributionNames: normal | | | | | | | | | Width: NaN |
| 73 | Accept | 0.16308 | 9.7631 | 0.10769 | 0.12684 | ensemble | Method: LogitBoost | | | | | | | | | NumLearningCycles: 217 | | | | | | | | | MinLeafSize: 70 |
| 74 | Accept | 0.10769 | 12.552 | 0.10769 | 0.12684 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 257 | | | | | | | | | MinLeafSize: 2 |
| 75 | Accept | 0.21474 | 0.091663 | 0.10769 | 0.12684 | knn | NumNeighbors: 49 |
| 76 | Accept | 0.11077 | 10.027 | 0.10769 | 0.12684 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 221 | | | | | | | | | MinLeafSize: 5 |
| 77 | Accept | 0.16 | 8.1869 | 0.10769 | 0.12684 | ensemble | Method: LogitBoost | | | | | | | | | NumLearningCycles: 203 | | | | | | | | | MinLeafSize: 1 |
| 78 | Accept | 0.11385 | 10.992 | 0.10769 | 0.12684 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 228 | | | | | | | | | MinLeafSize: 2 |
| 79 | Accept | 0.15692 | 12.232 | 0.10769 | 0.12684 | ensemble | Method: LogitBoost | | | | | | | | | NumLearningCycles: 293 | | | | | | | | | MinLeafSize: 1 |
| 80 | Accept | 0.12923 | 10.523 | 0.10769 | 0.12684 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 222 | | | | | | | | | MinLeafSize: 5 |
|====================================================================================================================| | Iter | Eval | Objective | Objective | BestSoFar | BestSoFar | Learner | Hyperparameter: Value | | | result | | runtime | (observed) | (estim.) | | | |====================================================================================================================| | 81 | Accept | 0.12308 | 11.281 | 0.10769 | 0.12684 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 233 | | | | | | | | | MinLeafSize: 11 |
| 82 | Accept | 0.11385 | 14.304 | 0.10769 | 0.12684 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 290 | | | | | | | | | MinLeafSize: 3 |
| 83 | Accept | 0.10769 | 12.236 | 0.10769 | 0.11724 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 260 | | | | | | | | | MinLeafSize: 1 |
| 84 | Best | 0.10154 | 12.57 | 0.10154 | 0.11441 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 256 | | | | | | | | | MinLeafSize: 1 |
| 85 | Accept | 0.16308 | 11.827 | 0.10154 | 0.11544 | ensemble | Method: LogitBoost | | | | | | | | | NumLearningCycles: 257 | | | | | | | | | MinLeafSize: 2 |
| 86 | Accept | 0.10769 | 14.658 | 0.10154 | 0.11317 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 295 | | | | | | | | | MinLeafSize: 2 |
| 87 | Accept | 0.10769 | 12.049 | 0.10154 | 0.11209 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 255 | | | | | | | | | MinLeafSize: 2 |
| 88 | Accept | 0.10462 | 12.346 | 0.10154 | 0.11125 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 261 | | | | | | | | | MinLeafSize: 1 |
| 89 | Accept | 0.11077 | 12.551 | 0.10154 | 0.10964 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 263 | | | | | | | | | MinLeafSize: 1 |
| 90 | Accept | 0.10769 | 12.461 | 0.10154 | 0.10895 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 257 | | | | | | | | | MinLeafSize: 1 |
| 91 | Accept | 0.10769 | 12.168 | 0.10154 | 0.10907 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 254 | | | | | | | | | MinLeafSize: 1 |
| 92 | Accept | 0.10462 | 12.397 | 0.10154 | 0.10811 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 257 | | | | | | | | | MinLeafSize: 1 |
| 93 | Accept | 0.16308 | 11.293 | 0.10154 | 0.10882 | ensemble | Method: LogitBoost | | | | | | | | | NumLearningCycles: 255 | | | | | | | | | MinLeafSize: 1 |
| 94 | Accept | 0.10769 | 12.364 | 0.10154 | 0.10734 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 255 | | | | | | | | | MinLeafSize: 1 |
| 95 | Accept | 0.11385 | 12.225 | 0.10154 | 0.10821 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 255 | | | | | | | | | MinLeafSize: 1 |
| 96 | Accept | 0.10769 | 12.369 | 0.10154 | 0.10809 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 257 | | | | | | | | | MinLeafSize: 1 |
| 97 | Accept | 0.11077 | 12.488 | 0.10154 | 0.10714 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 258 | | | | | | | | | MinLeafSize: 1 |
| 98 | Accept | 0.10769 | 12.439 | 0.10154 | 0.10802 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 257 | | | | | | | | | MinLeafSize: 2 |
| 99 | Accept | 0.10769 | 12.361 | 0.10154 | 0.1069 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 257 | | | | | | | | | MinLeafSize: 2 |
| 100 | Accept | 0.11692 | 12.373 | 0.10154 | 0.10736 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 256 | | | | | | | | | MinLeafSize: 2 |
|====================================================================================================================| | Iter | Eval | Objective | Objective | BestSoFar | BestSoFar | Learner | Hyperparameter: Value | | | result | | runtime | (observed) | (estim.) | | | |====================================================================================================================| | 101 | Accept | 0.16308 | 10.816 | 0.10154 | 0.10799 | ensemble | Method: LogitBoost | | | | | | | | | NumLearningCycles: 257 | | | | | | | | | MinLeafSize: 1 |
| 102 | Accept | 0.11385 | 9.9363 | 0.10154 | 0.10789 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 210 | | | | | | | | | MinLeafSize: 1 |
| 103 | Accept | 0.11385 | 10.353 | 0.10154 | 0.11056 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 256 | | | | | | | | | MinLeafSize: 1 |
| 104 | Accept | 0.12 | 10.401 | 0.10154 | 0.11008 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 257 | | | | | | | | | MinLeafSize: 1 |
| 105 | Accept | 0.11077 | 11.72 | 0.10154 | 0.10913 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 293 | | | | | | | | | MinLeafSize: 1 |
| 106 | Accept | 0.10769 | 10.452 | 0.10154 | 0.10878 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 256 | | | | | | | | | MinLeafSize: 1 |
| 107 | Accept | 0.10769 | 10.517 | 0.10154 | 0.10934 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 257 | | | | | | | | | MinLeafSize: 1 |
| 108 | Accept | 0.11077 | 8.3766 | 0.10154 | 0.10939 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 207 | | | | | | | | | MinLeafSize: 1 |
| 109 | Accept | 0.10769 | 10.549 | 0.10154 | 0.10883 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 261 | | | | | | | | | MinLeafSize: 1 |
| 110 | Accept | 0.11692 | 10.478 | 0.10154 | 0.10961 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 259 | | | | | | | | | MinLeafSize: 4 |
| 111 | Accept | 0.11385 | 12.05 | 0.10154 | 0.10869 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 295 | | | | | | | | | MinLeafSize: 2 |
| 112 | Accept | 0.12 | 11.95 | 0.10154 | 0.1083 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 294 | | | | | | | | | MinLeafSize: 1 |
| 113 | Accept | 0.11385 | 10.329 | 0.10154 | 0.10891 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 255 | | | | | | | | | MinLeafSize: 1 |
| 114 | Accept | 0.11077 | 11.196 | 0.10154 | 0.10922 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 260 | | | | | | | | | MinLeafSize: 2 |
| 115 | Accept | 0.10462 | 12.305 | 0.10154 | 0.10804 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 257 | | | | | | | | | MinLeafSize: 1 |
| 116 | Accept | 0.11385 | 12.352 | 0.10154 | 0.10886 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 257 | | | | | | | | | MinLeafSize: 4 |
| 117 | Best | 0.098462 | 9.241 | 0.098462 | 0.10878 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 201 | | | | | | | | | MinLeafSize: 1 |
| 118 | Accept | 0.10769 | 9.6282 | 0.098462 | 0.10917 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 201 | | | | | | | | | MinLeafSize: 1 |
| 119 | Accept | 0.11077 | 8.5028 | 0.098462 | 0.10922 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 201 | | | | | | | | | MinLeafSize: 1 |
| 120 | Accept | 0.12615 | 9.0029 | 0.098462 | 0.10942 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 201 | | | | | | | | | MinLeafSize: 1 |
|====================================================================================================================| | Iter | Eval | Objective | Objective | BestSoFar | BestSoFar | Learner | Hyperparameter: Value | | | result | | runtime | (observed) | (estim.) | | | |====================================================================================================================| | 121 | Accept | 0.11385 | 10.752 | 0.098462 | 0.10963 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 257 | | | | | | | | | MinLeafSize: 2 |
| 122 | Accept | 0.10769 | 10.986 | 0.098462 | 0.1089 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 256 | | | | | | | | | MinLeafSize: 1 |
| 123 | Accept | 0.10462 | 10.936 | 0.098462 | 0.10864 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 257 | | | | | | | | | MinLeafSize: 1 |
| 124 | Accept | 0.11077 | 10.384 | 0.098462 | 0.10889 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 257 | | | | | | | | | MinLeafSize: 1 |
| 125 | Accept | 0.10769 | 10.391 | 0.098462 | 0.10903 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 257 | | | | | | | | | MinLeafSize: 1 |
| 126 | Accept | 0.11385 | 8.3687 | 0.098462 | 0.10863 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 202 | | | | | | | | | MinLeafSize: 1 |
| 127 | Accept | 0.11385 | 10.703 | 0.098462 | 0.10838 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 261 | | | | | | | | | MinLeafSize: 1 |
| 128 | Accept | 0.11077 | 9.8426 | 0.098462 | 0.10872 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 243 | | | | | | | | | MinLeafSize: 1 |
| 129 | Accept | 0.11077 | 8.1229 | 0.098462 | 0.10861 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 202 | | | | | | | | | MinLeafSize: 1 |
| 130 | Accept | 0.11385 | 10.625 | 0.098462 | 0.10918 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 256 | | | | | | | | | MinLeafSize: 1 |
| 131 | Accept | 0.11077 | 10.54 | 0.098462 | 0.10936 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 254 | | | | | | | | | MinLeafSize: 1 |
| 132 | Accept | 0.11077 | 10.936 | 0.098462 | 0.10902 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 257 | | | | | | | | | MinLeafSize: 1 |
| 133 | Accept | 0.12923 | 10.934 | 0.098462 | 0.1098 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 257 | | | | | | | | | MinLeafSize: 1 |
| 134 | Accept | 0.11077 | 10.599 | 0.098462 | 0.10985 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 256 | | | | | | | | | MinLeafSize: 1 |
| 135 | Accept | 0.10769 | 8.3652 | 0.098462 | 0.10981 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 200 | | | | | | | | | MinLeafSize: 1 |
| 136 | Accept | 0.10769 | 8.6695 | 0.098462 | 0.1097 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 201 | | | | | | | | | MinLeafSize: 2 |
| 137 | Accept | 0.11385 | 8.1568 | 0.098462 | 0.10997 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 200 | | | | | | | | | MinLeafSize: 1 |
| 138 | Accept | 0.10769 | 8.1314 | 0.098462 | 0.10951 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 200 | | | | | | | | | MinLeafSize: 2 |
| 139 | Accept | 0.11385 | 8.1919 | 0.098462 | 0.11039 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 200 | | | | | | | | | MinLeafSize: 1 |
| 140 | Accept | 0.11385 | 11.537 | 0.098462 | 0.10919 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 261 | | | | | | | | | MinLeafSize: 2 |
|====================================================================================================================| | Iter | Eval | Objective | Objective | BestSoFar | BestSoFar | Learner | Hyperparameter: Value | | | result | | runtime | (observed) | (estim.) | | | |====================================================================================================================| | 141 | Accept | 0.10769 | 8.5051 | 0.098462 | 0.10985 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 201 | | | | | | | | | MinLeafSize: 2 |
| 142 | Accept | 0.11692 | 9.7003 | 0.098462 | 0.10953 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 236 | | | | | | | | | MinLeafSize: 2 |
| 143 | Accept | 0.10462 | 11.018 | 0.098462 | 0.10932 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 262 | | | | | | | | | MinLeafSize: 1 |
| 144 | Accept | 0.12308 | 11.197 | 0.098462 | 0.10945 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 262 | | | | | | | | | MinLeafSize: 1 |
| 145 | Accept | 0.11692 | 8.3426 | 0.098462 | 0.11056 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 201 | | | | | | | | | MinLeafSize: 2 |
| 146 | Accept | 0.11692 | 8.728 | 0.098462 | 0.10939 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 201 | | | | | | | | | MinLeafSize: 4 |
| 147 | Accept | 0.11692 | 10.952 | 0.098462 | 0.11014 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 259 | | | | | | | | | MinLeafSize: 1 |
| 148 | Accept | 0.12 | 8.5123 | 0.098462 | 0.10999 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 201 | | | | | | | | | MinLeafSize: 1 |
| 149 | Accept | 0.11077 | 10.694 | 0.098462 | 0.11039 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 257 | | | | | | | | | MinLeafSize: 2 |
| 150 | Accept | 0.10769 | 10.999 | 0.098462 | 0.10991 | ensemble | Method: Bag | | | | | | | | | NumLearningCycles: 260 | | | | | | | | | MinLeafSize: 1 |
__________________________________________________________ Optimization completed. MaxObjectiveEvaluations of 150 reached. Total function evaluations: 150 Total elapsed time: 1758.9912 seconds. Total objective function evaluation time: 985.2563 Best observed feasible point is an ensemble model with: Method: Bag NumLearningCycles: 201 MinLeafSize: 1 Observed objective function value = 0.098462 Estimated objective function value = 0.11265 Function evaluation time = 9.241 Best estimated feasible point (according to models) is an ensemble model with: Method: Bag NumLearningCycles: 256 MinLeafSize: 1 Estimated objective function value = 0.10991 Estimated function evaluation time = 11.0259
Итоговая модель возвращена fitcauto соответствует лучшей предполагаемой допустимой точке. Прежде, чем возвратить модель, функция переобучает его с помощью целых обучающих данных (carsTrain), перечисленный Learner (или модель) тип и отображенные гиперзначения параметров.
Оцените производительность набора тестов
Оцените производительность модели на наборе тестов.
testAccuracy = 1 - loss(Mdl,carsTest,'Origin')testAccuracy = 0.9520
confusionchart(carsTest.Origin,predict(Mdl,carsTest))
Автоматически выберите классификатор Используя матричные данные
Используйте fitcauto автоматически выбрать модель классификации оптимизированными гиперпараметрами, учитывая предиктор и данные об ответе, хранимые в отдельных переменных.
Загрузка данных
Загрузите humanactivity набор данных. Этот набор данных содержит 24 075 наблюдений за пятью физической деятельностью человека: Находясь (1), Стоя (2), Идя (3), Запускаясь (4), и Танцуя (5). Каждое наблюдение имеет 60 функций, извлеченных из ускоряющих данных, измеренных датчиками акселерометра смартфона. Переменная feat содержит матрицу данных предиктора 60 функций этих 24 075 наблюдений и переменную отклика actid содержит идентификаторы действия для наблюдений как целые числа.
load humanactivityДанные о разделе
Разделите данные в наборы обучающих данных и наборы тестов. Используйте 90% наблюдений, чтобы выбрать модель, и 10% наблюдений, чтобы подтвердить итоговую модель, возвращенную fitcauto. Используйте 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);
Запустите fitcauto
Передайте обучающие данные fitcauto. По умолчанию, fitcauto определяет соответствующую модель (или ученик) типы, чтобы попробовать, использует Байесовую оптимизацию, чтобы найти хорошие гиперзначения параметров для тех моделей и возвращает обученную модель с лучшей ожидаемой производительностью. Задайте, чтобы запуститься, оптимизация параллельно (требует Parallel Computing Toolbox™). Возвратите второй выход OptimizationResults это содержит детали Байесовой оптимизации.
Ожидайте, что этот процесс выбора модели займет время.
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.
|==============================================================================================================================| | Iter | Active | Eval | Objective | Objective | BestSoFar | BestSoFar | Learner | Hyperparameter: Value | | | workers | result | | runtime | (observed) | (estim.) | | | |==============================================================================================================================| | 1 | 6 | Best | 0.28088 | 51.451 | 0.28088 | 0.28088 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.22686 | | | | | | | | | | KernelScale: 330.4 |
| 2 | 5 | Accept | 0.036413 | 60.195 | 0.025845 | 0.11438 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 254 | | | | | | | | | | MinLeafSize: 1786 | | | | | | | | | | MaxNumSplits: 12 | | 3 | 5 | Best | 0.025845 | 5.5008 | 0.025845 | 0.11438 | tree | MinLeafSize: 59 |
| 4 | 6 | Best | 0.017722 | 6.6481 | 0.017722 | 0.021702 | tree | MinLeafSize: 9 |
| 5 | 6 | Accept | 0.017722 | 6.4868 | 0.017722 | 0.020592 | tree | MinLeafSize: 9 |
| 6 | 6 | Best | 0.0064611 | 79.739 | 0.0064611 | 0.020592 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 214 | | | | | | | | | | MinLeafSize: 5 | | | | | | | | | | MaxNumSplits: 23 |
| 7 | 6 | Accept | 0.050212 | 1.278 | 0.0064611 | 0.020592 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |
| 8 | 6 | Accept | 0.050212 | 1.2476 | 0.0064611 | 0.020592 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |
| 9 | 6 | Accept | 0.02266 | 189.95 | 0.0064611 | 0.020592 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 218 | | | | | | | | | | MinLeafSize: 2 | | | | | | | | | | MaxNumSplits: 63 |
| 10 | 6 | Accept | 0.033598 | 203.62 | 0.0064611 | 0.020592 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 264 | | | | | | | | | | MinLeafSize: 7 | | | | | | | | | | MaxNumSplits: 36 |
| 11 | 6 | Accept | 0.59166 | 28.752 | 0.0064611 | 0.020592 | nb | DistributionNames: kernel | | | | | | | | | | Width: 4.7212e-14 |
| 12 | 6 | Accept | 0.04389 | 155.77 | 0.0064611 | 0.020592 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.068467 | | | | | | | | | | KernelScale: 117.01 |
| 13 | 6 | Accept | 0.021599 | 60.254 | 0.0064611 | 0.020592 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 243 | | | | | | | | | | MinLeafSize: 1247 | | | | | | | | | | MaxNumSplits: 45 |
| 14 | 6 | Accept | 0.043567 | 23.25 | 0.0064611 | 0.020592 | knn | NumNeighbors: 144 |
| 15 | 6 | Accept | 0.028844 | 22.102 | 0.0064611 | 0.020592 | knn | NumNeighbors: 18 |
| 16 | 6 | Accept | 0.024598 | 22.408 | 0.0064611 | 0.020592 | knn | NumNeighbors: 7 |
| 17 | 6 | Accept | 0.03009 | 21.623 | 0.0064611 | 0.020592 | knn | NumNeighbors: 27 |
| 18 | 6 | Accept | 0.016891 | 7.2949 | 0.0064611 | 0.019464 | tree | MinLeafSize: 2 |
| 19 | 6 | Accept | 0.040059 | 4.3418 | 0.0064611 | 0.022968 | tree | MinLeafSize: 166 |
| 20 | 6 | Accept | 0.060319 | 2.5459 | 0.0064611 | 0.023364 | tree | MinLeafSize: 1881 |
|==============================================================================================================================| | Iter | Active | Eval | Objective | Objective | BestSoFar | BestSoFar | Learner | Hyperparameter: Value | | | workers | result | | runtime | (observed) | (estim.) | | | |==============================================================================================================================| | 21 | 6 | Accept | 0.050212 | 1.1819 | 0.0064611 | 0.023364 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |
| 22 | 6 | Accept | 0.036552 | 21.442 | 0.0064611 | 0.023364 | knn | NumNeighbors: 67 |
| 23 | 6 | Accept | 0.050212 | 0.69192 | 0.0064611 | 0.023364 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |
| 24 | 6 | Accept | 0.11076 | 37.67 | 0.0064611 | 0.023364 | knn | NumNeighbors: 2637 |
| 25 | 6 | Accept | 0.28711 | 69.473 | 0.0064611 | 0.030365 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 287 | | | | | | | | | | MinLeafSize: 4344 | | | | | | | | | | MaxNumSplits: 48 |
| 26 | 6 | Accept | 0.58127 | 32.81 | 0.0064611 | 0.030365 | nb | DistributionNames: kernel | | | | | | | | | | Width: 1.6293e-06 |
| 27 | 6 | Accept | 0.015784 | 7.406 | 0.0064611 | 0.027375 | tree | MinLeafSize: 1 |
| 28 | 6 | Accept | 0.59166 | 383.03 | 0.0064611 | 0.027375 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 790.4 | | | | | | | | | | KernelScale: 0.014348 |
| 29 | 6 | Accept | 0.069319 | 1.9964 | 0.0064611 | 0.034633 | tree | MinLeafSize: 2284 |
| 30 | 6 | Accept | 0.043336 | 3.5634 | 0.0064611 | 0.033826 | tree | MinLeafSize: 432 |
| 31 | 6 | Accept | 0.10555 | 35.914 | 0.0064611 | 0.033826 | knn | NumNeighbors: 2430 |
| 32 | 6 | Accept | 0.021183 | 6.3954 | 0.0064611 | 0.021851 | tree | MinLeafSize: 17 |
| 33 | 6 | Accept | 0.050212 | 1.098 | 0.0064611 | 0.021851 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |
| 34 | 6 | Accept | 0.014353 | 62.709 | 0.0064611 | 0.021851 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 234 | | | | | | | | | | MinLeafSize: 587 | | | | | | | | | | MaxNumSplits: 11 |
| 35 | 6 | Accept | 0.043428 | 191.69 | 0.0064611 | 0.021851 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 288 | | | | | | | | | | MinLeafSize: 45 | | | | | | | | | | MaxNumSplits: 23 |
| 36 | 6 | Accept | 0.4226 | 559.17 | 0.0064611 | 0.021851 | nb | DistributionNames: kernel | | | | | | | | | | Width: 72.906 |
| 37 | 6 | Accept | 0.74165 | 25.64 | 0.0064611 | 0.021851 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 217 | | | | | | | | | | MinLeafSize: 8856 | | | | | | | | | | MaxNumSplits: 36 |
| 38 | 6 | Accept | 0.59166 | 27.996 | 0.0064611 | 0.021851 | nb | DistributionNames: kernel | | | | | | | | | | Width: 1.191e-07 |
| 39 | 6 | Best | 0.0041074 | 91.985 | 0.0041074 | 0.021851 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 210 | | | | | | | | | | MinLeafSize: 129 | | | | | | | | | | MaxNumSplits: 100 |
| 40 | 6 | Accept | 0.73985 | 542.35 | 0.0041074 | 0.021851 | nb | DistributionNames: kernel | | | | | | | | | | Width: 1055.8 |
|==============================================================================================================================| | Iter | Active | Eval | Objective | Objective | BestSoFar | BestSoFar | Learner | Hyperparameter: Value | | | workers | result | | runtime | (observed) | (estim.) | | | |==============================================================================================================================| | 41 | 5 | Accept | 0.57615 | 353.08 | 0.0041074 | 0.021851 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 2.2347 | | | | | | | | | | KernelScale: 0.16176 | | 42 | 5 | Accept | 0.087087 | 32.51 | 0.0041074 | 0.021851 | knn | NumNeighbors: 1634 |
| 43 | 6 | Accept | 0.050212 | 0.85611 | 0.0041074 | 0.021851 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |
| 44 | 6 | Accept | 0.050212 | 0.75838 | 0.0041074 | 0.021851 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |
| 45 | 6 | Accept | 0.025891 | 21.86 | 0.0041074 | 0.021851 | knn | NumNeighbors: 6 |
| 46 | 6 | Accept | 0.030414 | 162.3 | 0.0041074 | 0.021851 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 206 | | | | | | | | | | MinLeafSize: 5 | | | | | | | | | | MaxNumSplits: 39 |
| 47 | 6 | Accept | 0.03286 | 178.49 | 0.0041074 | 0.021851 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 234 | | | | | | | | | | MinLeafSize: 73 | | | | | | | | | | MaxNumSplits: 43 |
| 48 | 6 | Accept | 0.037244 | 156.66 | 0.0041074 | 0.021851 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 5.9571 | | | | | | | | | | KernelScale: 840.87 |
| 49 | 6 | Accept | 0.01703 | 7.0967 | 0.0041074 | 0.020426 | tree | MinLeafSize: 4 |
| 50 | 6 | Accept | 0.017168 | 6.8667 | 0.0041074 | 0.0183 | tree | MinLeafSize: 5 |
| 51 | 6 | Accept | 0.039321 | 221.28 | 0.0041074 | 0.0183 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 299 | | | | | | | | | | MinLeafSize: 31 | | | | | | | | | | MaxNumSplits: 25 |
| 52 | 6 | Accept | 0.046474 | 199.35 | 0.0041074 | 0.0183 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 2.9119 | | | | | | | | | | KernelScale: 12.771 |
| 53 | 6 | Accept | 0.59166 | 433.93 | 0.0041074 | 0.0183 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 92.735 | | | | | | | | | | KernelScale: 0.0010711 |
| 54 | 6 | Accept | 0.054135 | 3.7916 | 0.0041074 | 0.018419 | tree | MinLeafSize: 783 |
| 55 | 6 | Accept | 0.049797 | 27.695 | 0.0041074 | 0.018419 | knn | NumNeighbors: 331 |
| 56 | 6 | Accept | 0.046566 | 26.856 | 0.0041074 | 0.018419 | knn | NumNeighbors: 193 |
| 57 | 6 | Accept | 0.049197 | 188.34 | 0.0041074 | 0.018419 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 253 | | | | | | | | | | MinLeafSize: 14 | | | | | | | | | | MaxNumSplits: 22 |
| 58 | 6 | Accept | 0.022706 | 25.765 | 0.0041074 | 0.018419 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 1.3505 | | | | | | | | | | KernelScale: 127.55 |
| 59 | 6 | Accept | 0.028798 | 5.7904 | 0.0041074 | 0.018856 | tree | MinLeafSize: 84 |
| 60 | 6 | Accept | 0.041351 | 29.766 | 0.0041074 | 0.018856 | knn | NumNeighbors: 124 |
|==============================================================================================================================| | Iter | Active | Eval | Objective | Objective | BestSoFar | BestSoFar | Learner | Hyperparameter: Value | | | workers | result | | runtime | (observed) | (estim.) | | | |==============================================================================================================================| | 61 | 6 | Accept | 0.030044 | 28.239 | 0.0041074 | 0.018856 | knn | NumNeighbors: 26 |
| 62 | 6 | Accept | 0.11838 | 295.65 | 0.0041074 | 0.018856 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.0027874 | | | | | | | | | | KernelScale: 33.944 |
| 63 | 6 | Accept | 0.47116 | 55.67 | 0.0041074 | 0.018856 | nb | DistributionNames: kernel | | | | | | | | | | Width: 4.7553e-05 |
| 64 | 6 | Accept | 0.26574 | 104.02 | 0.0041074 | 0.018856 | nb | DistributionNames: kernel | | | | | | | | | | Width: 0.0011441 |
| 65 | 6 | Accept | 0.050212 | 0.69002 | 0.0041074 | 0.018856 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |
| 66 | 6 | Accept | 0.017168 | 6.7802 | 0.0041074 | 0.018674 | tree | MinLeafSize: 5 |
| 67 | 6 | Accept | 0.077072 | 281.55 | 0.0041074 | 0.018674 | nb | DistributionNames: kernel | | | | | | | | | | Width: 0.026902 |
| 68 | 6 | Accept | 0.031613 | 26.638 | 0.0041074 | 0.018674 | knn | NumNeighbors: 33 |
| 69 | 6 | Accept | 0.02003 | 108.02 | 0.0041074 | 0.018674 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 609.47 | | | | | | | | | | KernelScale: 39.88 |
| 70 | 6 | Accept | 0.14141 | 92.623 | 0.0041074 | 0.018674 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 292 | | | | | | | | | | MinLeafSize: 3870 | | | | | | | | | | MaxNumSplits: 33 |
| 71 | 6 | Accept | 0.01103 | 74.107 | 0.0041074 | 0.018674 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 214 | | | | | | | | | | MinLeafSize: 203 | | | | | | | | | | MaxNumSplits: 14 |
| 72 | 6 | Accept | 0.0093225 | 78.987 | 0.0041074 | 0.018674 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 225 | | | | | | | | | | MinLeafSize: 461 | | | | | | | | | | MaxNumSplits: 45 |
| 73 | 6 | Accept | 0.0081226 | 75.993 | 0.0041074 | 0.018674 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 212 | | | | | | | | | | MinLeafSize: 224 | | | | | | | | | | MaxNumSplits: 19 |
| 74 | 6 | Accept | 0.0086302 | 75.677 | 0.0041074 | 0.011486 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 206 | | | | | | | | | | MinLeafSize: 372 | | | | | | | | | | MaxNumSplits: 66 |
| 75 | 6 | Accept | 0.012461 | 69.727 | 0.0041074 | 0.0099707 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 219 | | | | | | | | | | MinLeafSize: 602 | | | | | | | | | | MaxNumSplits: 16 |
| 76 | 6 | Accept | 0.011907 | 66.364 | 0.0041074 | 0.018674 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 206 | | | | | | | | | | MinLeafSize: 535 | | | | | | | | | | MaxNumSplits: 17 |
| 77 | 6 | Accept | 0.066227 | 125.89 | 0.0041074 | 0.0096155 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 210 | | | | | | | | | | MinLeafSize: 579 | | | | | | | | | | MaxNumSplits: 44 |
| 78 | 6 | Accept | 0.014168 | 64.533 | 0.0041074 | 0.018674 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 207 | | | | | | | | | | MinLeafSize: 670 | | | | | | | | | | MaxNumSplits: 21 |
| 79 | 6 | Accept | 0.012276 | 73.034 | 0.0041074 | 0.0096795 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 228 | | | | | | | | | | MinLeafSize: 614 | | | | | | | | | | MaxNumSplits: 94 |
| 80 | 6 | Accept | 0.017353 | 61.294 | 0.0041074 | 0.0094804 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 204 | | | | | | | | | | MinLeafSize: 772 | | | | | | | | | | MaxNumSplits: 13 |
|==============================================================================================================================| | Iter | Active | Eval | Objective | Objective | BestSoFar | BestSoFar | Learner | Hyperparameter: Value | | | workers | result | | runtime | (observed) | (estim.) | | | |==============================================================================================================================| | 81 | 6 | Accept | 0.01846 | 61.652 | 0.0041074 | 0.0084959 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 211 | | | | | | | | | | MinLeafSize: 879 | | | | | | | | | | MaxNumSplits: 79 |
| 82 | 6 | Accept | 0.016937 | 67.027 | 0.0041074 | 0.0092617 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 227 | | | | | | | | | | MinLeafSize: 805 | | | | | | | | | | MaxNumSplits: 20 |
| 83 | 6 | Accept | 0.012876 | 256.48 | 0.0041074 | 0.0092617 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 501.55 | | | | | | | | | | KernelScale: 190.02 |
| 84 | 6 | Accept | 0.017537 | 61.538 | 0.0041074 | 0.0091728 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 207 | | | | | | | | | | MinLeafSize: 836 | | | | | | | | | | MaxNumSplits: 40 |
| 85 | 6 | Accept | 0.01463 | 43.6 | 0.0041074 | 0.0091728 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 397.97 | | | | | | | | | | KernelScale: 310.04 |
| 86 | 6 | Accept | 0.025522 | 24.217 | 0.0041074 | 0.0091728 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 36.55 | | | | | | | | | | KernelScale: 658.03 |
| 87 | 6 | Accept | 0.01703 | 68.966 | 0.0041074 | 0.0092732 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 230 | | | | | | | | | | MinLeafSize: 822 | | | | | | | | | | MaxNumSplits: 22 |
| 88 | 6 | Accept | 0.021137 | 114.19 | 0.0041074 | 0.0092732 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 593.64 | | | | | | | | | | KernelScale: 37.983 |
| 89 | 6 | Accept | 0.59166 | 2089.7 | 0.0041074 | 0.0092732 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.045413 | | | | | | | | | | KernelScale: 0.0034709 |
| 90 | 6 | Accept | 0.019799 | 57.157 | 0.0041074 | 0.0089505 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 202 | | | | | | | | | | MinLeafSize: 962 | | | | | | | | | | MaxNumSplits: 81 |
| 91 | 6 | Accept | 0.085472 | 118.41 | 0.0041074 | 0.0089505 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 855.69 | | | | | | | | | | KernelScale: 6.5486 |
| 92 | 6 | Accept | 0.02003 | 59.729 | 0.0041074 | 0.0095371 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 216 | | | | | | | | | | MinLeafSize: 1004 | | | | | | | | | | MaxNumSplits: 12 |
| 93 | 6 | Accept | 0.019845 | 58.677 | 0.0041074 | 0.0095584 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 208 | | | | | | | | | | MinLeafSize: 926 | | | | | | | | | | MaxNumSplits: 32 |
| 94 | 6 | Accept | 0.021506 | 59.315 | 0.0041074 | 0.011473 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 216 | | | | | | | | | | MinLeafSize: 1063 | | | | | | | | | | MaxNumSplits: 82 |
| 95 | 6 | Accept | 0.022383 | 55.893 | 0.0041074 | 0.0091004 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 205 | | | | | | | | | | MinLeafSize: 1150 | | | | | | | | | | MaxNumSplits: 11 |
| 96 | 6 | Accept | 0.054966 | 273.69 | 0.0041074 | 0.0091004 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 550 | | | | | | | | | | KernelScale: 10.56 |
| 97 | 6 | Accept | 0.021322 | 64.607 | 0.0041074 | 0.0089091 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 229 | | | | | | | | | | MinLeafSize: 1111 | | | | | | | | | | MaxNumSplits: 52 |
| 98 | 6 | Accept | 0.021691 | 60.128 | 0.0041074 | 0.0083855 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 213 | | | | | | | | | | MinLeafSize: 1112 | | | | | | | | | | MaxNumSplits: 14 |
| 99 | 6 | Accept | 0.59166 | 2658.6 | 0.0041074 | 0.0083855 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.98129 | | | | | | | | | | KernelScale: 0.0054937 |
| 100 | 6 | Accept | 0.22499 | 820.13 | 0.0041074 | 0.0083855 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 869.37 | | | | | | | | | | KernelScale: 1.5553 |
|==============================================================================================================================| | Iter | Active | Eval | Objective | Objective | BestSoFar | BestSoFar | Learner | Hyperparameter: Value | | | workers | result | | runtime | (observed) | (estim.) | | | |==============================================================================================================================| | 101 | 6 | Accept | 0.021691 | 63.07 | 0.0041074 | 0.008821 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 228 | | | | | | | | | | MinLeafSize: 1180 | | | | | | | | | | MaxNumSplits: 83 |
| 102 | 6 | Accept | 0.02206 | 57.161 | 0.0041074 | 0.0095876 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 207 | | | | | | | | | | MinLeafSize: 1208 | | | | | | | | | | MaxNumSplits: 17 |
| 103 | 6 | Accept | 0.022014 | 59.267 | 0.0041074 | 0.0094147 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 217 | | | | | | | | | | MinLeafSize: 1187 | | | | | | | | | | MaxNumSplits: 61 |
| 104 | 6 | Accept | 0.23763 | 216.82 | 0.0041074 | 0.0094147 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 512.6 | | | | | | | | | | KernelScale: 1.2736 |
| 105 | 6 | Accept | 0.050212 | 0.76452 | 0.0041074 | 0.0094147 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |
| 106 | 6 | Accept | 0.023352 | 259.58 | 0.0041074 | 0.010382 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 220 | | | | | | | | | | MinLeafSize: 1372 | | | | | | | | | | MaxNumSplits: 96 |
| 107 | 6 | Accept | 0.022983 | 60.428 | 0.0041074 | 0.0092473 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 219 | | | | | | | | | | MinLeafSize: 1293 | | | | | | | | | | MaxNumSplits: 25 |
| 108 | 6 | Accept | 0.022568 | 62.238 | 0.0041074 | 0.0094314 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 231 | | | | | | | | | | MinLeafSize: 1344 | | | | | | | | | | MaxNumSplits: 19 |
| 109 | 6 | Accept | 0.025383 | 59.558 | 0.0041074 | 0.0091873 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 222 | | | | | | | | | | MinLeafSize: 1404 | | | | | | | | | | MaxNumSplits: 12 |
| 110 | 6 | Accept | 0.022522 | 65.564 | 0.0041074 | 0.009405 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 243 | | | | | | | | | | MinLeafSize: 1360 | | | | | | | | | | MaxNumSplits: 14 |
| 111 | 6 | Accept | 0.022799 | 61.264 | 0.0041074 | 0.0091171 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 226 | | | | | | | | | | MinLeafSize: 1356 | | | | | | | | | | MaxNumSplits: 90 |
| 112 | 6 | Accept | 0.058935 | 45.569 | 0.0041074 | 0.0096787 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 207 | | | | | | | | | | MinLeafSize: 2521 | | | | | | | | | | MaxNumSplits: 33 |
| 113 | 6 | Accept | 0.022937 | 64.08 | 0.0041074 | 0.0087354 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 231 | | | | | | | | | | MinLeafSize: 1383 | | | | | | | | | | MaxNumSplits: 88 |
| 114 | 6 | Accept | 0.027783 | 59.671 | 0.0041074 | 0.012899 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 224 | | | | | | | | | | MinLeafSize: 1551 | | | | | | | | | | MaxNumSplits: 20 |
| 115 | 6 | Accept | 0.027737 | 53.366 | 0.0041074 | 0.0096795 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 203 | | | | | | | | | | MinLeafSize: 1548 | | | | | | | | | | MaxNumSplits: 11 |
| 116 | 6 | Accept | 0.027137 | 62.9 | 0.0041074 | 0.009529 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 227 | | | | | | | | | | MinLeafSize: 1479 | | | | | | | | | | MaxNumSplits: 10 |
| 117 | 6 | Accept | 0.59166 | 3642 | 0.0041074 | 0.009529 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 658.37 | | | | | | | | | | KernelScale: 0.0016161 |
| 118 | 6 | Accept | 0.23163 | 1370.3 | 0.0041074 | 0.009529 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 720.53 | | | | | | | | | | KernelScale: 1.1039 |
| 119 | 6 | Accept | 0.027183 | 58.796 | 0.0041074 | 0.0097206 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 223 | | | | | | | | | | MinLeafSize: 1544 | | | | | | | | | | MaxNumSplits: 25 |
| 120 | 6 | Accept | 0.027091 | 62.912 | 0.0041074 | 0.0086543 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 238 | | | | | | | | | | MinLeafSize: 1499 | | | | | | | | | | MaxNumSplits: 42 |
|==============================================================================================================================| | Iter | Active | Eval | Objective | Objective | BestSoFar | BestSoFar | Learner | Hyperparameter: Value | | | workers | result | | runtime | (observed) | (estim.) | | | |==============================================================================================================================| | 121 | 6 | Accept | 0.59166 | 3676.1 | 0.0041074 | 0.0086543 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 17.982 | | | | | | | | | | KernelScale: 0.0043756 |
| 122 | 6 | Accept | 0.027783 | 56.988 | 0.0041074 | 0.0095236 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 225 | | | | | | | | | | MinLeafSize: 1551 | | | | | | | | | | MaxNumSplits: 38 |
| 123 | 6 | Accept | 0.22896 | 1222.6 | 0.0041074 | 0.0095236 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 30.726 | | | | | | | | | | KernelScale: 1.2194 |
| 124 | 6 | Accept | 0.028706 | 53.614 | 0.0041074 | 0.0094105 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 213 | | | | | | | | | | MinLeafSize: 1571 | | | | | | | | | | MaxNumSplits: 64 |
| 125 | 6 | Accept | 0.026906 | 68.419 | 0.0041074 | 0.0096214 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 256 | | | | | | | | | | MinLeafSize: 1548 | | | | | | | | | | MaxNumSplits: 72 |
| 126 | 6 | Accept | 0.040705 | 51.812 | 0.0041074 | 0.0096155 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 206 | | | | | | | | | | MinLeafSize: 2028 | | | | | | | | | | MaxNumSplits: 12 |
| 127 | 6 | Accept | 0.10901 | 491.59 | 0.0041074 | 0.0096155 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 317.07 | | | | | | | | | | KernelScale: 3.345 |
| 128 | 6 | Accept | 0.038813 | 50.737 | 0.0041074 | 0.0094473 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 215 | | | | | | | | | | MinLeafSize: 1857 | | | | | | | | | | MaxNumSplits: 77 |
| 129 | 6 | Accept | 0.04269 | 56.006 | 0.0041074 | 0.01155 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 250 | | | | | | | | | | MinLeafSize: 2192 | | | | | | | | | | MaxNumSplits: 17 |
| 130 | 6 | Best | 0.0040151 | 144.99 | 0.0040151 | 0.0076249 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 286 | | | | | | | | | | MinLeafSize: 133 | | | | | | | | | | MaxNumSplits: 91 |
| 131 | 6 | Accept | 0.039505 | 54.552 | 0.0040151 | 0.0093087 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 234 | | | | | | | | | | MinLeafSize: 2016 | | | | | | | | | | MaxNumSplits: 15 |
| 132 | 6 | Accept | 0.039921 | 49.255 | 0.0040151 | 0.0077926 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 215 | | | | | | | | | | MinLeafSize: 1997 | | | | | | | | | | MaxNumSplits: 77 |
| 133 | 6 | Accept | 0.048182 | 48.846 | 0.0040151 | 0.0086785 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 213 | | | | | | | | | | MinLeafSize: 2243 | | | | | | | | | | MaxNumSplits: 13 |
| 134 | 6 | Accept | 0.041859 | 66.081 | 0.0040151 | 0.0079912 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 263 | | | | | | | | | | MinLeafSize: 2099 | | | | | | | | | | MaxNumSplits: 32 |
| 135 | 6 | Accept | 0.046105 | 68.865 | 0.0040151 | 0.0078968 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 287 | | | | | | | | | | MinLeafSize: 2342 | | | | | | | | | | MaxNumSplits: 12 |
| 136 | 6 | Accept | 0.0042459 | 150.03 | 0.0040151 | 0.008766 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 297 | | | | | | | | | | MinLeafSize: 163 | | | | | | | | | | MaxNumSplits: 92 |
| 137 | 6 | Accept | 0.046289 | 62.375 | 0.0040151 | 0.012639 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 273 | | | | | | | | | | MinLeafSize: 2260 | | | | | | | | | | MaxNumSplits: 67 |
| 138 | 6 | Accept | 0.044028 | 53.269 | 0.0040151 | 0.0092674 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 224 | | | | | | | | | | MinLeafSize: 2190 | | | | | | | | | | MaxNumSplits: 12 |
| 139 | 6 | Accept | 0.04892 | 45.442 | 0.0040151 | 0.006892 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 207 | | | | | | | | | | MinLeafSize: 2354 | | | | | | | | | | MaxNumSplits: 17 |
| 140 | 6 | Accept | 0.11515 | 99.696 | 0.0040151 | 0.0093722 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 281 | | | | | | | | | | MinLeafSize: 2297 | | | | | | | | | | MaxNumSplits: 30 |
|==============================================================================================================================| | Iter | Active | Eval | Objective | Objective | BestSoFar | BestSoFar | Learner | Hyperparameter: Value | | | workers | result | | runtime | (observed) | (estim.) | | | |==============================================================================================================================| | 141 | 6 | Accept | 0.10845 | 91.18 | 0.0040151 | 0.0065827 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 254 | | | | | | | | | | MinLeafSize: 2275 | | | | | | | | | | MaxNumSplits: 37 |
| 142 | 6 | Accept | 0.74165 | 34.944 | 0.0040151 | 0.0072537 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 253 | | | | | | | | | | MinLeafSize: 8804 | | | | | | | | | | MaxNumSplits: 11 |
| 143 | 6 | Best | 0.0038767 | 158.75 | 0.0038767 | 0.0057394 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 294 | | | | | | | | | | MinLeafSize: 106 | | | | | | | | | | MaxNumSplits: 94 |
| 144 | 6 | Accept | 0.04592 | 64.846 | 0.0038767 | 0.0062301 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 290 | | | | | | | | | | MinLeafSize: 2326 | | | | | | | | | | MaxNumSplits: 34 |
| 145 | 6 | Accept | 0.047074 | 55.822 | 0.0038767 | 0.0062084 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 257 | | | | | | | | | | MinLeafSize: 2345 | | | | | | | | | | MaxNumSplits: 60 |
| 146 | 6 | Accept | 0.04832 | 50.283 | 0.0038767 | 0.0092604 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 227 | | | | | | | | | | MinLeafSize: 2350 | | | | | | | | | | MaxNumSplits: 19 |
| 147 | 6 | Accept | 0.016107 | 26.377 | 0.0038767 | 0.0092604 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 94.982 | | | | | | | | | | KernelScale: 222.61 |
| 148 | 6 | Accept | 0.050166 | 53.779 | 0.0038767 | 0.0068092 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 233 | | | | | | | | | | MinLeafSize: 2407 | | | | | | | | | | MaxNumSplits: 20 |
| 149 | 6 | Accept | 0.058196 | 46.596 | 0.0038767 | 0.0063746 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 210 | | | | | | | | | | MinLeafSize: 2469 | | | | | | | | | | MaxNumSplits: 17 |
| 150 | 6 | Accept | 0.067519 | 48.158 | 0.0038767 | 0.0065889 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 233 | | | | | | | | | | MinLeafSize: 3054 | | | | | | | | | | MaxNumSplits: 68 |
__________________________________________________________ Optimization completed. MaxObjectiveEvaluations of 150 reached. Total function evaluations: 150 Total elapsed time: 5515.7636 seconds. Total objective function evaluation time: 27568.0193 Best observed feasible point is an ensemble model with: Method: AdaBoostM2 NumLearningCycles: 294 MinLeafSize: 106 MaxNumSplits: 94 Observed objective function value = 0.0038767 Estimated objective function value = 0.006912 Function evaluation time = 158.7495 Best estimated feasible point (according to models) is an ensemble model with: Method: AdaBoostM2 NumLearningCycles: 210 MinLeafSize: 129 MaxNumSplits: 100 Estimated objective function value = 0.0065889 Estimated function evaluation time = 129.4444
Итоговая модель возвращена fitcauto соответствует лучшей предполагаемой допустимой точке. Прежде, чем возвратить модель, функция переобучает его с помощью целых обучающих данных (XTrain и YTrain), перечисленный Learner (или модель) тип и отображенные гиперзначения параметров.
Оцените производительность набора тестов
Оцените итоговую производительность модели на наборе тестовых данных.
testAccuracy = 1 - loss(Mdl,XTest,YTest)
testAccuracy = 0.9963
Итоговая модель правильно классифицирует более чем 99% наблюдений.
Объедините выбор признаков и автоматизированный выбор классификатора
Используйте fitcauto автоматически выбрать модель классификации оптимизированными гиперпараметрами, учитывая предиктор и данные об ответе, хранимые в таблице. Перед передающими данными к fitcauto, выполните выбор признаков, чтобы удалить неважные предикторы из набора данных.
Загрузка и данные о разделе
Считайте файл примера CreditRating_Historical.dat в таблицу. Данные о предикторе состоят из финансовых отношений и информации об отрасли промышленности для списка корпоративных клиентов. Переменная отклика состоит из кредитных рейтингов, присвоенных рейтинговым агентством. Предварительно просмотрите первые несколько 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' }
Поскольку 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. Удалите ID переменная из таблицы, и преобразует Industry переменная к categorical переменная.
creditrating = removevars(creditrating,'ID');
creditrating.Industry = categorical(creditrating.Industry);Разделите данные в наборы обучающих данных и наборы тестов. Используйте приблизительно 85% наблюдений для выбора модели и настраивающего процесса гиперпараметра, и 15% наблюдений, чтобы проверить производительность итоговой модели, возвращенной fitcauto на новых данных. Используйте cvpartition разделить данные.
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,:);
Выполните выбор признаков
Прежде, чем передать обучающие данные fitcauto, найдите важные предикторы при помощи fscchi2 функция. Визуализируйте баллы предиктора при помощи bar функция. Поскольку некоторыми баллами может быть Inf, и bar отбрасывания Inf значения, постройте конечные баллы сначала и затем постройте конечное представление Inf баллы в различном 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'})
Заметьте что Industry предиктор имеет низкий балл, соответствующий p-значению, которое больше 0.05, который указывает на тот Industry не может быть важная функция. Удалите Industry покажите от наборов тестовых данных и обучения.
creditTrain = removevars(creditTrain,'Industry'); creditTest = removevars(creditTest,'Industry');
Запустите fitcauto
fitcauto не поддерживает таблицы для линейного или модели классификации ядер. Чтобы включать они моделируют (или ученик) типы, преобразуют данные о предикторе в матричную форму прежде, чем передать обучающие данные fitcauto. Преобразуйте тестовые данные также. Это преобразование возможно, потому что все остающиеся предикторы являются числовыми.
predictorTrain = removevars(creditTrain,'Rating'); XTrain = table2array(predictorTrain); YTrain = creditTrain.Rating; predictorTest = removevars(creditTest,'Rating'); XTest = table2array(predictorTest); YTest = creditTest.Rating;
Передайте обучающие данные fitcauto. Функция использует Байесовую оптимизацию, чтобы выбрать модели и их гиперзначения параметров, и возвращает обученную модель Mdl с лучшей ожидаемой производительностью. Задайте, чтобы попробовать все доступные типы ученика и запуститься, оптимизация параллельно (требует Parallel Computing Toolbox™). Возвратите второй выход Results это содержит детали Байесовой оптимизации.
Ожидайте, что этот процесс займет время.
options = struct('UseParallel',true); [Mdl,Results] = fitcauto(XTrain,YTrain, ... '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.
|==============================================================================================================================| | Iter | Active | Eval | Objective | Objective | BestSoFar | BestSoFar | Learner | Hyperparameter: Value | | | workers | result | | runtime | (observed) | (estim.) | | | |==============================================================================================================================| | 1 | 6 | Best | 0.42716 | 2.8649 | 0.42716 | 0.42716 | discr | Delta: 0.00046441 | | | | | | | | | | Gamma: 0.2485 |
| 2 | 4 | Accept | 0.74185 | 5.0967 | 0.24948 | 0.42911 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.48455 | | | | | | | | | | KernelScale: 354.44 | | 3 | 4 | Best | 0.24948 | 4.9586 | 0.24948 | 0.42911 | linear | Coding: onevsone | | | | | | | | | | Lambda: 6.3551e-08 | | | | | | | | | | Learner: logistic | | 4 | 4 | Accept | 0.29794 | 3.246 | 0.24948 | 0.42911 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 12 | | | | | | | | | | LearnRate: 0.063776 | | | | | | | | | | MinLeafSize: 277 |
| 5 | 3 | Best | 0.24708 | 9.215 | 0.24708 | 0.36903 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 7.8433 | | | | | | | | | | Lambda: 1.4468e-06 | | 6 | 3 | Accept | 0.25067 | 0.65429 | 0.24708 | 0.36903 | knn | NumNeighbors: 105 | | | | | | | | | | Distance: minkowski |
| 7 | 6 | Accept | 0.52917 | 2.9984 | 0.24708 | 0.63551 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.002417 | | | | | | | | | | KernelScale: 356.9 |
| 8 | 3 | Accept | 0.55818 | 0.61959 | 0.24708 | 0.58763 | discr | Delta: 0.98612 | | | | | | | | | | Gamma: 0.86519 | | 9 | 3 | Accept | 0.3781 | 1.7268 | 0.24708 | 0.58763 | linear | Coding: onevsall | | | | | | | | | | Lambda: 1.0412e-06 | | | | | | | | | | Learner: logistic | | 10 | 3 | Accept | 0.43225 | 0.73455 | 0.24708 | 0.58763 | discr | Delta: 0.00013711 | | | | | | | | | | Gamma: 0.60585 | | 11 | 3 | Accept | 0.47712 | 4.1471 | 0.24708 | 0.58763 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 2.7347 | | | | | | | | | | KernelScale: 24.465 |
| 12 | 6 | Accept | 0.25695 | 2.4614 | 0.24708 | 0.58763 | nb | DistributionNames: kernel | | | | | | | | | | Width: 0.057566 |
| 13 | 3 | Accept | 0.26383 | 0.54745 | 0.24379 | 0.58763 | tree | MinLeafSize: 30 | | 14 | 3 | Accept | 0.42327 | 0.84715 | 0.24379 | 0.58763 | knn | NumNeighbors: 56 | | | | | | | | | | Distance: cosine | | 15 | 3 | Best | 0.24379 | 2.0052 | 0.24379 | 0.58763 | linear | Coding: onevsone | | | | | | | | | | Lambda: 5.9172e-05 | | | | | | | | | | Learner: svm | | 16 | 3 | Accept | 0.82112 | 5.0159 | 0.24379 | 0.58763 | kernel | Coding: onevsall | | | | | | | | | | KernelScale: 0.0043375 | | | | | | | | | | Lambda: 0.0023789 |
| 17 | 6 | Accept | 0.45169 | 1.0941 | 0.24379 | 0.32154 | linear | Coding: onevsall | | | | | | | | | | Lambda: 0.0028505 | | | | | | | | | | Learner: svm |
| 18 | 3 | Accept | 0.53365 | 2.9722 | 0.24379 | 0.31096 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.0022255 | | | | | | | | | | KernelScale: 206.47 | | 19 | 3 | Accept | 0.74185 | 0.69997 | 0.24379 | 0.31096 | discr | Delta: 788.29 | | | | | | | | | | Gamma: 0.096315 | | 20 | 3 | Accept | 0.39186 | 3.7201 | 0.24379 | 0.31096 | kernel | Coding: onevsall | | | | | | | | | | KernelScale: 22.787 | | | | | | | | | | Lambda: 4.7789e-05 | |==============================================================================================================================| | Iter | Active | Eval | Objective | Objective | BestSoFar | BestSoFar | Learner | Hyperparameter: Value | | | workers | result | | runtime | (observed) | (estim.) | | | |==============================================================================================================================| | 21 | 3 | Accept | 0.24439 | 1.7527 | 0.24379 | 0.31096 | linear | Coding: onevsone | | | | | | | | | | Lambda: 1.9056e-08 | | | | | | | | | | Learner: svm |
| 22 | 6 | Accept | 0.4834 | 0.42353 | 0.24379 | 0.31096 | knn | NumNeighbors: 72 | | | | | | | | | | Distance: correlation |
| 23 | 3 | Accept | 0.74185 | 0.12006 | 0.24379 | 0.31096 | tree | MinLeafSize: 1558 | | 24 | 3 | Accept | 0.27042 | 0.67315 | 0.24379 | 0.31096 | tree | MinLeafSize: 76 | | 25 | 3 | Accept | 0.45887 | 4.6976 | 0.24379 | 0.31096 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 7.1247 | | | | | | | | | | KernelScale: 0.9781 | | 26 | 3 | Accept | 0.28208 | 0.88675 | 0.24379 | 0.31096 | knn | NumNeighbors: 291 | | | | | | | | | | Distance: minkowski |
| 27 | 6 | Accept | 0.43255 | 0.13008 | 0.24379 | 0.31096 | discr | Delta: 0.016844 | | | | | | | | | | Gamma: 0.64466 |
| 28 | 4 | Accept | 0.66796 | 0.25188 | 0.24379 | 0.31096 | knn | NumNeighbors: 77 | | | | | | | | | | Distance: jaccard | | 29 | 4 | Accept | 0.28059 | 0.44707 | 0.24379 | 0.31096 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN | | 30 | 4 | Accept | 0.65869 | 0.38657 | 0.24379 | 0.31096 | knn | NumNeighbors: 61 | | | | | | | | | | Distance: jaccard |
| 31 | 3 | Accept | 0.74185 | 6.6038 | 0.24379 | 0.31096 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 0.0010962 | | | | | | | | | | Lambda: 0.035691 | | 32 | 3 | Accept | 0.27789 | 0.11562 | 0.24379 | 0.31096 | tree | MinLeafSize: 94 |
| 33 | 6 | Accept | 0.74185 | 0.096361 | 0.24379 | 0.31096 | discr | Delta: 244.12 | | | | | | | | | | Gamma: 0.23748 |
| 34 | 3 | Accept | 0.32456 | 0.18499 | 0.24349 | 0.31096 | tree | MinLeafSize: 3 | | 35 | 3 | Accept | 0.63506 | 2.4392 | 0.24349 | 0.31096 | knn | NumNeighbors: 1563 | | | | | | | | | | Distance: mahalanobis | | 36 | 3 | Best | 0.24349 | 2.1919 | 0.24349 | 0.31096 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.044076 | | | | | | | | | | KernelScale: 0.035497 | | 37 | 3 | Accept | 0.6216 | 2.1779 | 0.24349 | 0.31096 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.055096 | | | | | | | | | | KernelScale: 6.2342 |
| 38 | 6 | Accept | 0.42208 | 0.1616 | 0.24349 | 0.31096 | discr | Delta: 0.0090118 | | | | | | | | | | Gamma: 0.062207 |
| 39 | 4 | Accept | 0.47173 | 3.2018 | 0.24349 | 0.31096 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 3.7197 | | | | | | | | | | KernelScale: 2.9509 | | 40 | 4 | Accept | 0.74185 | 2.4165 | 0.24349 | 0.31096 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.019393 | | | | | | | | | | KernelScale: 332.27 | |==============================================================================================================================| | Iter | Active | Eval | Objective | Objective | BestSoFar | BestSoFar | Learner | Hyperparameter: Value | | | workers | result | | runtime | (observed) | (estim.) | | | |==============================================================================================================================| | 41 | 4 | Accept | 0.53126 | 2.5238 | 0.24349 | 0.31096 | knn | NumNeighbors: 372 | | | | | | | | | | Distance: mahalanobis |
| 42 | 4 | Accept | 0.25965 | 15.125 | 0.24349 | 0.27803 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 150 | | | | | | | | | | LearnRate: 0.014842 | | | | | | | | | | MinLeafSize: 21 |
| 43 | 6 | Accept | 0.74185 | 4.4109 | 0.24349 | 0.27803 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 24.681 | | | | | | | | | | Lambda: 0.092669 |
| 44 | 3 | Accept | 0.26413 | 22.354 | 0.24349 | 0.27803 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 304 | | | | | | | | | | LearnRate: NaN | | | | | | | | | | MinLeafSize: 100 | | 45 | 3 | Accept | 0.24499 | 14.917 | 0.24349 | 0.27803 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.019387 | | | | | | | | | | KernelScale: 0.0047515 | | 46 | 3 | Accept | 0.74185 | 4.3649 | 0.24349 | 0.27803 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 24.681 | | | | | | | | | | Lambda: 0.092669 | | 47 | 3 | Accept | 0.74185 | 7.1585 | 0.24349 | 0.27803 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 24.681 | | | | | | | | | | Lambda: 0.092669 |
| 48 | 6 | Accept | 0.28059 | 0.18491 | 0.24349 | 0.27255 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |
| 49 | 3 | Accept | 0.60754 | 3.9106 | 0.24349 | 0.27255 | kernel | Coding: onevsall | | | | | | | | | | KernelScale: 176.2 | | | | | | | | | | Lambda: 2.3903e-06 | | 50 | 3 | Accept | 0.42507 | 0.11067 | 0.24349 | 0.27255 | discr | Delta: 0.25925 | | | | | | | | | | Gamma: 0.82918 | | 51 | 3 | Accept | 0.28806 | 4.3596 | 0.24349 | 0.27255 | ensemble | Method: RUSBoost | | | | | | | | | | NumLearningCycles: 42 | | | | | | | | | | LearnRate: 0.0031729 | | | | | | | | | | MinLeafSize: 33 | | 52 | 3 | Accept | 0.47024 | 3.0918 | 0.24349 | 0.27255 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.0028932 | | | | | | | | | | KernelScale: 0.47789 |
| 53 | 6 | Accept | 0.49477 | 3.6628 | 0.24349 | 0.27255 | kernel | Coding: onevsall | | | | | | | | | | KernelScale: 0.093586 | | | | | | | | | | Lambda: 0.0050756 |
| 54 | 4 | Accept | 0.27131 | 0.15291 | 0.24349 | 0.27548 | tree | MinLeafSize: 20 | | 55 | 4 | Accept | 0.28059 | 0.46186 | 0.24349 | 0.27548 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN | | 56 | 4 | Accept | 0.28059 | 0.45079 | 0.24349 | 0.27548 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |
| 57 | 4 | Accept | 0.32456 | 0.17695 | 0.24349 | 0.27548 | tree | MinLeafSize: 3 |
| 58 | 4 | Accept | 0.33144 | 1.2562 | 0.24349 | 0.2843 | nb | DistributionNames: kernel | | | | | | | | | | Width: 0.0020049 |
| 59 | 3 | Accept | 0.43314 | 23.917 | 0.24349 | 0.28235 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 357 | | | | | | | | | | LearnRate: 0.035928 | | | | | | | | | | MinLeafSize: 799 | | 60 | 3 | Accept | 0.28059 | 0.13198 | 0.24349 | 0.28235 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |
|==============================================================================================================================| | Iter | Active | Eval | Objective | Objective | BestSoFar | BestSoFar | Learner | Hyperparameter: Value | | | workers | result | | runtime | (observed) | (estim.) | | | |==============================================================================================================================| | 61 | 6 | Accept | 0.26234 | 0.55057 | 0.24349 | 0.28235 | knn | NumNeighbors: 157 | | | | | | | | | | Distance: euclidean |
| 62 | 4 | Accept | 0.31917 | 10.817 | 0.24349 | 0.28235 | ensemble | Method: RUSBoost | | | | | | | | | | NumLearningCycles: 132 | | | | | | | | | | LearnRate: 0.0014516 | | | | | | | | | | MinLeafSize: 104 | | 63 | 4 | Accept | 0.24529 | 5.709 | 0.24349 | 0.28235 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 4.6933 | | | | | | | | | | Lambda: 9.8945e-07 | | 64 | 4 | Accept | 0.43255 | 0.74626 | 0.24349 | 0.28235 | linear | Coding: onevsall | | | | | | | | | | Lambda: 2.7304e-07 | | | | | | | | | | Learner: svm |
| 65 | 4 | Accept | 0.57972 | 2.8035 | 0.24349 | 0.28235 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.12255 | | | | | | | | | | KernelScale: 81.172 |
| 66 | 4 | Accept | 0.26383 | 0.13419 | 0.24349 | 0.28235 | knn | NumNeighbors: 13 | | | | | | | | | | Distance: chebychev |
| 67 | 3 | Accept | 0.24469 | 17.277 | 0.24349 | 0.28235 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.062223 | | | | | | | | | | KernelScale: 0.0077043 | | 68 | 3 | Accept | 0.42596 | 0.095102 | 0.24349 | 0.28235 | discr | Delta: 9.4222e-06 | | | | | | | | | | Gamma: 0.15603 |
| 69 | 6 | Accept | 0.67783 | 0.67221 | 0.24349 | 0.28235 | linear | Coding: onevsall | | | | | | | | | | Lambda: 2.4732 | | | | | | | | | | Learner: logistic |
| 70 | 5 | Accept | 0.25695 | 0.16748 | 0.24349 | 0.28235 | knn | NumNeighbors: 14 | | | | | | | | | | Distance: cityblock | | 71 | 5 | Accept | 0.46276 | 0.7837 | 0.24349 | 0.28235 | linear | Coding: onevsall | | | | | | | | | | Lambda: 0.0033674 | | | | | | | | | | Learner: svm |
| 72 | 4 | Accept | 0.28448 | 6.0063 | 0.24349 | 0.27803 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 78 | | | | | | | | | | LearnRate: NaN | | | | | | | | | | MinLeafSize: 168 | | 73 | 4 | Accept | 0.6548 | 2.4452 | 0.24349 | 0.27803 | nb | DistributionNames: kernel | | | | | | | | | | Width: 2.6013 |
| 74 | 4 | Accept | 0.42926 | 0.1085 | 0.24349 | 0.27803 | discr | Delta: 0.041145 | | | | | | | | | | Gamma: 0.34864 |
| 75 | 2 | Accept | 0.24529 | 222.53 | 0.24349 | 0.27803 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.25488 | | | | | | | | | | KernelScale: 0.0037823 | | 76 | 2 | Accept | 0.25217 | 21.217 | 0.24349 | 0.27803 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 257 | | | | | | | | | | LearnRate: NaN | | | | | | | | | | MinLeafSize: 19 | | 77 | 2 | Accept | 0.32516 | 0.19708 | 0.24349 | 0.27803 | tree | MinLeafSize: 5 |
| 78 | 6 | Accept | 0.43703 | 0.87585 | 0.24349 | 0.27803 | linear | Coding: onevsall | | | | | | | | | | Lambda: 0.013265 | | | | | | | | | | Learner: logistic |
| 79 | 3 | Accept | 0.25695 | 2.1197 | 0.24349 | 0.27803 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 712.17 | | | | | | | | | | KernelScale: 79.244 | | 80 | 3 | Accept | 0.25456 | 2.276 | 0.24349 | 0.27803 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 22 | | | | | | | | | | LearnRate: 0.28501 | | | | | | | | | | MinLeafSize: 104 | |==============================================================================================================================| | Iter | Active | Eval | Objective | Objective | BestSoFar | BestSoFar | Learner | Hyperparameter: Value | | | workers | result | | runtime | (observed) | (estim.) | | | |==============================================================================================================================| | 81 | 3 | Accept | 0.31529 | 3.001 | 0.24349 | 0.27803 | ensemble | Method: RUSBoost | | | | | | | | | | NumLearningCycles: 22 | | | | | | | | | | LearnRate: 0.10996 | | | | | | | | | | MinLeafSize: 104 | | 82 | 3 | Accept | 0.42596 | 0.19157 | 0.24349 | 0.27803 | discr | Delta: 0.00034456 | | | | | | | | | | Gamma: 0.08223 |
| 83 | 5 | Accept | 0.25456 | 7.1816 | 0.24349 | 0.27803 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 0.78697 | | | | | | | | | | Lambda: 4.1197e-06 | | 84 | 5 | Accept | 0.43015 | 0.092979 | 0.24349 | 0.27803 | discr | Delta: 0.0069822 | | | | | | | | | | Gamma: 0.49526 |
| 85 | 3 | Accept | 0.32905 | 0.3807 | 0.24349 | 0.27803 | knn | NumNeighbors: 65 | | | | | | | | | | Distance: seuclidean | | 86 | 3 | Accept | 0.50194 | 0.58497 | 0.24349 | 0.27803 | linear | Coding: onevsall | | | | | | | | | | Lambda: 22.21 | | | | | | | | | | Learner: svm | | 87 | 3 | Accept | 0.30242 | 0.2907 | 0.24349 | 0.27803 | tree | MinLeafSize: 219 |
| 88 | 5 | Accept | 0.28328 | 11.457 | 0.24349 | 0.27803 | kernel | Coding: onevsall | | | | | | | | | | KernelScale: 0.24613 | | | | | | | | | | Lambda: 6.5582e-06 | | 89 | 5 | Accept | 0.67903 | 3.2891 | 0.24349 | 0.27803 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 66.432 | | | | | | | | | | Lambda: 0.00097982 |
| 90 | 3 | Accept | 0.28059 | 0.15967 | 0.24349 | 0.27803 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN | | 91 | 3 | Accept | 0.74185 | 0.1332 | 0.24349 | 0.27803 | discr | Delta: 89.479 | | | | | | | | | | Gamma: 0.11568 | | 92 | 3 | Accept | 0.29704 | 1.6362 | 0.24349 | 0.27803 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.90434 | | | | | | | | | | KernelScale: 5.1972 |
| 93 | 4 | Accept | 0.75232 | 6.2607 | 0.24349 | 0.27803 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 0.0025204 | | | | | | | | | | Lambda: 0.0017056 | | 94 | 4 | Accept | 0.61203 | 2.7767 | 0.24349 | 0.27803 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 53 | | | | | | | | | | LearnRate: NaN | | | | | | | | | | MinLeafSize: 926 | | 95 | 4 | Accept | 0.42926 | 0.1032 | 0.24349 | 0.27803 | discr | Delta: 0.00083172 | | | | | | | | | | Gamma: 0.41309 |
| 96 | 2 | Accept | 0.24619 | 3.3793 | 0.24349 | 0.2819 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 305.08 | | | | | | | | | | KernelScale: 1.4567 | | 97 | 2 | Accept | 0.71911 | 0.67791 | 0.24349 | 0.2819 | knn | NumNeighbors: 659 | | | | | | | | | | Distance: jaccard | | 98 | 2 | Accept | 0.37212 | 1.5638 | 0.24349 | 0.2819 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 15 | | | | | | | | | | LearnRate: 0.010706 | | | | | | | | | | MinLeafSize: 366 |
| 99 | 5 | Accept | 0.28059 | 0.1674 | 0.24349 | 0.2819 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN | | 100 | 5 | Accept | 0.30093 | 0.10739 | 0.24349 | 0.2819 | tree | MinLeafSize: 135 |
|==============================================================================================================================| | Iter | Active | Eval | Objective | Objective | BestSoFar | BestSoFar | Learner | Hyperparameter: Value | | | workers | result | | runtime | (observed) | (estim.) | | | |==============================================================================================================================| | 101 | 3 | Accept | 0.2767 | 5.3033 | 0.24349 | 0.2819 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 53 | | | | | | | | | | LearnRate: 0.012335 | | | | | | | | | | MinLeafSize: 5 | | 102 | 3 | Accept | 0.28597 | 2.062 | 0.24349 | 0.2819 | ensemble | Method: RUSBoost | | | | | | | | | | NumLearningCycles: 23 | | | | | | | | | | LearnRate: 0.018926 | | | | | | | | | | MinLeafSize: 1 | | 103 | 3 | Accept | 0.61771 | 0.24182 | 0.24349 | 0.2819 | tree | MinLeafSize: 1200 |
| 104 | 6 | Accept | 0.25576 | 3.0306 | 0.24349 | 0.27907 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 38 | | | | | | | | | | LearnRate: NaN | | | | | | | | | | MinLeafSize: 28 |
| 105 | 3 | Accept | 0.31977 | 1.4678 | 0.24349 | 0.27907 | linear | Coding: onevsone | | | | | | | | | | Lambda: 0.040102 | | | | | | | | | | Learner: logistic | | 106 | 3 | Accept | 0.28059 | 0.15767 | 0.24349 | 0.27907 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN | | 107 | 3 | Accept | 0.52976 | 3.7522 | 0.24349 | 0.27907 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 52 | | | | | | | | | | LearnRate: 0.0051271 | | | | | | | | | | MinLeafSize: 854 | | 108 | 3 | Accept | 0.3781 | 1.876 | 0.24349 | 0.27907 | linear | Coding: onevsall | | | | | | | | | | Lambda: 9.0139e-07 | | | | | | | | | | Learner: logistic |
| 109 | 6 | Best | 0.2429 | 2.1357 | 0.2429 | 0.27907 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.10541 | | | | | | | | | | KernelScale: 0.061524 |
| 110 | 3 | Accept | 0.30212 | 15.324 | 0.2429 | 0.27907 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 249 | | | | | | | | | | LearnRate: NaN | | | | | | | | | | MinLeafSize: 292 | | 111 | 3 | Accept | 0.25905 | 6.4685 | 0.2429 | 0.27907 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 61 | | | | | | | | | | LearnRate: NaN | | | | | | | | | | MinLeafSize: 3 | | 112 | 3 | Accept | 0.4819 | 3.2352 | 0.2429 | 0.27907 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 239.8 | | | | | | | | | | KernelScale: 51.105 | | 113 | 3 | Accept | 0.72241 | 3.0692 | 0.2429 | 0.27907 | nb | DistributionNames: kernel | | | | | | | | | | Width: 7.4433 |
| 114 | 6 | Accept | 0.45947 | 0.55863 | 0.2429 | 0.27907 | linear | Coding: onevsall | | | | | | | | | | Lambda: 0.028513 | | | | | | | | | | Learner: svm |
| 115 | 3 | Accept | 0.74185 | 2.3519 | 0.2429 | 0.27907 | nb | DistributionNames: kernel | | | | | | | | | | Width: 74.975 | | 116 | 3 | Accept | 0.29794 | 1.7602 | 0.2429 | 0.27907 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 14 | | | | | | | | | | LearnRate: 0.0011077 | | | | | | | | | | MinLeafSize: 2 | | 117 | 3 | Accept | 0.48549 | 14.081 | 0.2429 | 0.27907 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.36208 | | | | | | | | | | KernelScale: 0.061675 | | 118 | 3 | Accept | 0.38169 | 3.4566 | 0.2429 | 0.27907 | kernel | Coding: onevsall | | | | | | | | | | KernelScale: 6.0043 | | | | | | | | | | Lambda: 0.0033315 |
| 119 | 6 | Accept | 0.24559 | 5.4474 | 0.2429 | 0.27907 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0059499 | | | | | | | | | | KernelScale: 0.0045622 |
| 120 | 4 | Best | 0.2408 | 1.6297 | 0.2408 | 0.27907 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.011671 | | | | | | | | | | KernelScale: 0.050076 | |==============================================================================================================================| | Iter | Active | Eval | Objective | Objective | BestSoFar | BestSoFar | Learner | Hyperparameter: Value | | | workers | result | | runtime | (observed) | (estim.) | | | |==============================================================================================================================| | 121 | 4 | Accept | 0.25725 | 15.492 | 0.2408 | 0.27907 | ensemble | Method: AdaBoostM2 | | | | | | | | | | NumLearningCycles: 149 | | | | | | | | | | LearnRate: 0.10812 | | | | | | | | | | MinLeafSize: 1 | | 122 | 4 | Accept | 0.47831 | 0.088834 | 0.2408 | 0.27907 | tree | MinLeafSize: 833 |
| 123 | 4 | Best | 0.2405 | 1.4439 | 0.2405 | 0.24636 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.015035 | | | | | | | | | | KernelScale: 0.057715 |
| 124 | 4 | Accept | 0.2417 | 1.5135 | 0.2405 | 0.24498 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0095934 | | | | | | | | | | KernelScale: 0.058685 |
| 125 | 4 | Best | 0.2399 | 1.4853 | 0.2399 | 0.24443 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.035844 | | | | | | | | | | KernelScale: 0.08166 |
| 126 | 4 | Accept | 0.24529 | 63.489 | 0.2399 | 0.24408 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 405.36 | | | | | | | | | | KernelScale: 0.3059 |
| 127 | 4 | Accept | 0.2417 | 1.4706 | 0.2399 | 0.24465 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.016665 | | | | | | | | | | KernelScale: 0.083583 |
| 128 | 4 | Accept | 0.25546 | 1.4908 | 0.2399 | 0.24411 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.038736 | | | | | | | | | | KernelScale: 0.55267 |
| 129 | 4 | Accept | 0.30841 | 1.885 | 0.2399 | 0.24408 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0015949 | | | | | | | | | | KernelScale: 0.34759 |
| 130 | 4 | Accept | 0.36494 | 1.9172 | 0.2399 | 0.24384 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.017084 | | | | | | | | | | KernelScale: 1.9122 |
| 131 | 4 | Accept | 0.30242 | 1.6419 | 0.2399 | 0.24389 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.033156 | | | | | | | | | | KernelScale: 1.1823 |
| 132 | 5 | Accept | 0.40203 | 1.8764 | 0.2399 | 0.24422 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.022866 | | | | | | | | | | KernelScale: 2.5154 |
| 133 | 5 | Accept | 0.24349 | 2.287 | 0.2399 | 0.24422 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.14375 | | | | | | | | | | KernelScale: 0.063056 |
| 134 | 5 | Accept | 0.2402 | 1.598 | 0.2399 | 0.24365 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.012787 | | | | | | | | | | KernelScale: 0.061711 |
| 135 | 4 | Accept | 0.4843 | 135.72 | 0.2399 | 0.24365 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.0016902 | | | | | | | | | | KernelScale: 0.031528 | | 136 | 4 | Accept | 0.78193 | 10.536 | 0.2399 | 0.24365 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 0.0024164 | | | | | | | | | | Lambda: 3.0566e-07 |
| 137 | 4 | Accept | 0.4499 | 4.2514 | 0.2399 | 0.24207 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.020039 | | | | | | | | | | KernelScale: 0.05327 |
| 138 | 4 | Accept | 0.79061 | 10.427 | 0.2399 | 0.24207 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 0.0012217 | | | | | | | | | | Lambda: 1.8732e-06 |
| 139 | 4 | Accept | 0.24379 | 1.5859 | 0.2399 | 0.2419 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0026657 | | | | | | | | | | KernelScale: 0.058261 |
| 140 | 4 | Accept | 0.24379 | 1.7337 | 0.2399 | 0.24721 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.02367 | | | | | | | | | | KernelScale: 0.037805 |
|==============================================================================================================================| | Iter | Active | Eval | Objective | Objective | BestSoFar | BestSoFar | Learner | Hyperparameter: Value | | | workers | result | | runtime | (observed) | (estim.) | | | |==============================================================================================================================| | 141 | 4 | Accept | 0.74604 | 9.898 | 0.2399 | 0.24721 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 0.03064 | | | | | | | | | | Lambda: 4.6992e-06 |
| 142 | 4 | Accept | 0.24619 | 4.1003 | 0.2399 | 0.24822 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.40789 | | | | | | | | | | KernelScale: 0.047603 |
| 143 | 4 | Accept | 0.24499 | 9.2411 | 0.2399 | 0.24541 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.80115 | | | | | | | | | | KernelScale: 0.038152 |
| 144 | 4 | Accept | 0.2405 | 1.389 | 0.2399 | 0.24287 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.018782 | | | | | | | | | | KernelScale: 0.06909 |
| 145 | 4 | Accept | 0.26234 | 4.1253 | 0.2399 | 0.24287 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 3.0021 | | | | | | | | | | Lambda: 0.002498 |
| 146 | 4 | Accept | 0.24559 | 1.3591 | 0.2399 | 0.24345 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0015537 | | | | | | | | | | KernelScale: 0.061601 |
| 147 | 4 | Accept | 0.39426 | 2.1758 | 0.2399 | 0.24345 | kernel | Coding: onevsall | | | | | | | | | | KernelScale: 2.7611 | | | | | | | | | | Lambda: 0.058785 |
| 148 | 4 | Accept | 0.44032 | 9.2619 | 0.2399 | 0.24345 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 0.10145 | | | | | | | | | | Lambda: 1.247e-06 |
| 149 | 4 | Accept | 0.26114 | 5.2181 | 0.2399 | 0.24345 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 12.896 | | | | | | | | | | Lambda: 5.2477e-07 |
| 150 | 4 | Accept | 0.50374 | 10.004 | 0.2399 | 0.24345 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 0.073856 | | | | | | | | | | Lambda: 1.1532e-06 |
| 151 | 4 | Accept | 0.27939 | 5.1821 | 0.2399 | 0.24345 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 16.882 | | | | | | | | | | Lambda: 3.1205e-07 |
| 152 | 4 | Accept | 0.27819 | 4.5003 | 0.2399 | 0.24345 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 18.796 | | | | | | | | | | Lambda: 1.2989e-05 |
| 153 | 4 | Accept | 0.27819 | 4.7288 | 0.2399 | 0.24345 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 20.531 | | | | | | | | | | Lambda: 7.0733e-07 |
| 154 | 4 | Accept | 0.64792 | 3.5128 | 0.2399 | 0.24345 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 13.339 | | | | | | | | | | Lambda: 0.015595 |
| 155 | 4 | Accept | 0.36135 | 4.5167 | 0.2399 | 0.24345 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 23.171 | | | | | | | | | | Lambda: 8.9366e-07 |
| 156 | 4 | Accept | 0.31947 | 4.9979 | 0.2399 | 0.24345 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 23.387 | | | | | | | | | | Lambda: 3.5105e-07 |
| 157 | 4 | Accept | 0.25935 | 4.7775 | 0.2399 | 0.24345 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 9.5518 | | | | | | | | | | Lambda: 4.9153e-06 |
| 158 | 4 | Accept | 0.30093 | 3.849 | 0.2399 | 0.24345 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 7.3599 | | | | | | | | | | Lambda: 0.0025979 |
| 159 | 4 | Accept | 0.35028 | 4.1896 | 0.2399 | 0.24345 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 23.149 | | | | | | | | | | Lambda: 8.8313e-06 |
| 160 | 4 | Accept | 0.24559 | 1.3885 | 0.2399 | 0.24519 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0027832 | | | | | | | | | | KernelScale: 0.069321 |
|==============================================================================================================================| | Iter | Active | Eval | Objective | Objective | BestSoFar | BestSoFar | Learner | Hyperparameter: Value | | | workers | result | | runtime | (observed) | (estim.) | | | |==============================================================================================================================| | 161 | 4 | Accept | 0.26324 | 4.97 | 0.2399 | 0.24519 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 11.576 | | | | | | | | | | Lambda: 5.1587e-06 |
| 162 | 3 | Accept | 0.24379 | 430.57 | 0.2399 | 0.24519 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 314.58 | | | | | | | | | | KernelScale: 0.055576 | | 163 | 3 | Accept | 0.26742 | 4.2066 | 0.2399 | 0.24519 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 9.9599 | | | | | | | | | | Lambda: 0.0002376 |
| 164 | 6 | Accept | 0.25546 | 7.3897 | 0.2399 | 0.24519 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 0.66256 | | | | | | | | | | Lambda: 4.9136e-06 |
| 165 | 3 | Accept | 0.29165 | 4.0149 | 0.2399 | 0.24519 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 8.7912 | | | | | | | | | | Lambda: 0.00065247 | | 166 | 3 | Accept | 0.28059 | 0.16557 | 0.2399 | 0.24519 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN | | 167 | 3 | Accept | 0.242 | 1.5393 | 0.2399 | 0.24519 | linear | Coding: onevsone | | | | | | | | | | Lambda: 4.114e-07 | | | | | | | | | | Learner: svm | | 168 | 3 | Accept | 0.66437 | 1.3826 | 0.2399 | 0.24519 | linear | Coding: onevsone | | | | | | | | | | Lambda: 1.5345 | | | | | | | | | | Learner: logistic |
| 169 | 6 | Accept | 0.30302 | 3.6462 | 0.2399 | 0.24519 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 10.456 | | | | | | | | | | Lambda: 0.0012729 |
| 170 | 3 | Accept | 0.36584 | 9.8828 | 0.2399 | 0.24519 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 0.12329 | | | | | | | | | | Lambda: 5.7761e-06 | | 171 | 3 | Accept | 0.2767 | 2.2185 | 0.2399 | 0.24519 | nb | DistributionNames: kernel | | | | | | | | | | Width: 0.013013 | | 172 | 3 | Accept | 0.29016 | 10.253 | 0.2399 | 0.24519 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 161 | | | | | | | | | | LearnRate: NaN | | | | | | | | | | MinLeafSize: 233 | | 173 | 3 | Accept | 0.30212 | 0.17493 | 0.2399 | 0.24519 | tree | MinLeafSize: 125 |
| 174 | 6 | Accept | 0.36853 | 3.9359 | 0.2399 | 0.24519 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 14.54 | | | | | | | | | | Lambda: 0.0018156 |
| 175 | 3 | Accept | 0.2399 | 1.5716 | 0.2399 | 0.24519 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.018425 | | | | | | | | | | KernelScale: 0.05816 | | 176 | 3 | Accept | 0.25426 | 2.1785 | 0.2399 | 0.24519 | ensemble | Method: Bag | | | | | | | | | | NumLearningCycles: 21 | | | | | | | | | | LearnRate: NaN | | | | | | | | | | MinLeafSize: 10 | | 177 | 3 | Accept | 0.44421 | 0.69127 | 0.2399 | 0.24519 | linear | Coding: onevsall | | | | | | | | | | Lambda: 9.8986e-08 | | | | | | | | | | Learner: svm | | 178 | 3 | Accept | 0.24349 | 4.7623 | 0.2399 | 0.24519 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 7.5839 | | | | | | | | | | Lambda: 6.1706e-06 |
| 179 | 6 | Accept | 0.32546 | 3.8654 | 0.2399 | 0.24519 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 11.818 | | | | | | | | | | Lambda: 0.0021157 |
| 180 | 3 | Accept | 0.25396 | 4.8254 | 0.2399 | 0.24519 | kernel | Coding: onevsall | | | | | | | | | | KernelScale: 3.2686 | | | | | | | | | | Lambda: 1.4612e-06 | |==============================================================================================================================| | Iter | Active | Eval | Objective | Objective | BestSoFar | BestSoFar | Learner | Hyperparameter: Value | | | workers | result | | runtime | (observed) | (estim.) | | | |==============================================================================================================================| | 181 | 3 | Accept | 0.51391 | 3.1699 | 0.2399 | 0.24519 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.11591 | | | | | | | | | | KernelScale: 495.17 | | 182 | 3 | Accept | 0.2417 | 1.6756 | 0.2399 | 0.24519 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 7.202 | | | | | | | | | | KernelScale: 1.5776 | | 183 | 3 | Accept | 0.28059 | 0.59112 | 0.2399 | 0.24519 | nb | DistributionNames: normal | | | | | | | | | | Width: NaN |
| 184 | 6 | Accept | 0.31259 | 3.7378 | 0.2399 | 0.24519 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 12.684 | | | | | | | | | | Lambda: 0.0015075 |
| 185 | 4 | Accept | 0.25277 | 12.458 | 0.2399 | 0.24519 | kernel | Coding: onevsall | | | | | | | | | | KernelScale: 0.66323 | | | | | | | | | | Lambda: 3.1391e-06 | | 186 | 4 | Accept | 0.74185 | 4.433 | 0.2399 | 0.24519 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 513.67 | | | | | | | | | | Lambda: 0.013813 | | 187 | 4 | Accept | 0.29165 | 9.8216 | 0.2399 | 0.24519 | kernel | Coding: onevsall | | | | | | | | | | KernelScale: 0.1806 | | | | | | | | | | Lambda: 5.7403e-05 |
| 188 | 4 | Accept | 0.24589 | 5.2677 | 0.2399 | 0.24519 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 4.5409 | | | | | | | | | | Lambda: 1.6179e-06 |
| 189 | 4 | Accept | 0.25875 | 4.9459 | 0.2399 | 0.24519 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 13.189 | | | | | | | | | | Lambda: 1.0438e-06 |
| 190 | 4 | Accept | 0.29794 | 9.4099 | 0.2399 | 0.24519 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 0.24389 | | | | | | | | | | Lambda: 1.5305e-06 |
| 191 | 4 | Accept | 0.2399 | 1.4613 | 0.2399 | 0.24482 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.017676 | | | | | | | | | | KernelScale: 0.05684 |
| 192 | 4 | Accept | 0.6198 | 2.1987 | 0.2399 | 0.24188 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.037629 | | | | | | | | | | KernelScale: 4.6813 |
| 193 | 4 | Accept | 0.24589 | 3.6814 | 0.2399 | 0.24395 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.48718 | | | | | | | | | | KernelScale: 0.071987 |
| 194 | 4 | Accept | 0.30362 | 1.6935 | 0.2399 | 0.24179 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.036257 | | | | | | | | | | KernelScale: 1.496 |
| 195 | 4 | Accept | 0.24828 | 5.6169 | 0.2399 | 0.24179 | kernel | Coding: onevsall | | | | | | | | | | KernelScale: 2.0998 | | | | | | | | | | Lambda: 1.7852e-06 |
| 196 | 4 | Accept | 0.2405 | 1.4008 | 0.2399 | 0.2416 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.025005 | | | | | | | | | | KernelScale: 0.079623 |
| 197 | 4 | Accept | 0.25695 | 10.376 | 0.2399 | 0.2416 | kernel | Coding: onevsall | | | | | | | | | | KernelScale: 0.54744 | | | | | | | | | | Lambda: 2.181e-06 |
| 198 | 4 | Accept | 0.2429 | 1.8889 | 0.2399 | 0.25253 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.090859 | | | | | | | | | | KernelScale: 0.066231 |
| 199 | 4 | Accept | 0.24858 | 4.1934 | 0.2399 | 0.25163 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 2.5004 | | | | | | | | | | Lambda: 0.0016318 |
| 200 | 4 | Accept | 0.2408 | 1.4334 | 0.2399 | 0.24216 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.029253 | | | | | | | | | | KernelScale: 0.063477 |
|==============================================================================================================================| | Iter | Active | Eval | Objective | Objective | BestSoFar | BestSoFar | Learner | Hyperparameter: Value | | | workers | result | | runtime | (observed) | (estim.) | | | |==============================================================================================================================| | 201 | 4 | Accept | 0.25067 | 1.4766 | 0.2399 | 0.24186 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.01517 | | | | | | | | | | KernelScale: 0.27169 |
| 202 | 4 | Accept | 0.29794 | 1.8479 | 0.2399 | 0.24143 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.20149 | | | | | | | | | | KernelScale: 2.5463 |
| 203 | 4 | Accept | 0.24589 | 6.5027 | 0.2399 | 0.24148 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.30831 | | | | | | | | | | KernelScale: 0.028787 |
| 204 | 4 | Accept | 0.24678 | 4.2346 | 0.2399 | 0.24148 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 1.5999 | | | | | | | | | | Lambda: 0.0022599 |
| 205 | 5 | Accept | 0.28148 | 4.267 | 0.2399 | 0.24148 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 3.1926 | | | | | | | | | | Lambda: 0.0039213 |
| 206 | 5 | Accept | 0.25366 | 4.6995 | 0.2399 | 0.24148 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 2.2861 | | | | | | | | | | Lambda: 0.0023336 |
| 207 | 6 | Accept | 0.28986 | 4.6245 | 0.2399 | 0.24148 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 2.8603 | | | | | | | | | | Lambda: 0.0072539 |
| 208 | 6 | Accept | 0.25067 | 4.9866 | 0.2399 | 0.24148 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 1.0901 | | | | | | | | | | Lambda: 0.0099896 |
| 209 | 6 | Accept | 0.26802 | 4.837 | 0.2399 | 0.24148 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 1.6265 | | | | | | | | | | Lambda: 0.01081 |
| 210 | 6 | Accept | 0.24499 | 56.624 | 0.2399 | 0.24154 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 8.7909 | | | | | | | | | | KernelScale: 0.05728 |
| 211 | 6 | Accept | 0.25097 | 5.1646 | 0.2399 | 0.24154 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 0.94502 | | | | | | | | | | Lambda: 0.012321 |
| 212 | 6 | Accept | 0.27879 | 4.737 | 0.2399 | 0.24154 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 1.5962 | | | | | | | | | | Lambda: 0.014927 |
| 213 | 6 | Accept | 0.28537 | 4.6966 | 0.2399 | 0.24154 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 1.858 | | | | | | | | | | Lambda: 0.012473 |
| 214 | 5 | Accept | 0.24529 | 172.16 | 0.2399 | 0.24154 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 32.749 | | | | | | | | | | KernelScale: 0.061248 | | 215 | 5 | Accept | 0.25127 | 4.7942 | 0.2399 | 0.24154 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 0.91593 | | | | | | | | | | Lambda: 0.013134 |
| 216 | 5 | Accept | 0.277 | 4.1458 | 0.2399 | 0.24154 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 1.4949 | | | | | | | | | | Lambda: 0.015008 |
| 217 | 5 | Accept | 0.30123 | 3.9237 | 0.2399 | 0.24154 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 1.2069 | | | | | | | | | | Lambda: 0.071401 |
| 218 | 5 | Accept | 0.30691 | 2.555 | 0.2399 | 0.24154 | kernel | Coding: onevsall | | | | | | | | | | KernelScale: 1.0328 | | | | | | | | | | Lambda: 0.013653 |
| 219 | 5 | Accept | 0.29106 | 4.1175 | 0.2399 | 0.24154 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 1.9718 | | | | | | | | | | Lambda: 0.014791 |
| 220 | 5 | Accept | 0.27341 | 4.054 | 0.2399 | 0.24154 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 1.0948 | | | | | | | | | | Lambda: 0.029433 |
|==============================================================================================================================| | Iter | Active | Eval | Objective | Objective | BestSoFar | BestSoFar | Learner | Hyperparameter: Value | | | workers | result | | runtime | (observed) | (estim.) | | | |==============================================================================================================================| | 221 | 5 | Accept | 0.24499 | 1.7385 | 0.2399 | 0.24325 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.019711 | | | | | | | | | | KernelScale: 0.20418 |
| 222 | 5 | Accept | 0.32576 | 3.5611 | 0.2399 | 0.24325 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 0.96406 | | | | | | | | | | Lambda: 0.19447 |
| 223 | 5 | Accept | 0.33084 | 4.3505 | 0.2399 | 0.24325 | kernel | Coding: onevsall | | | | | | | | | | KernelScale: 23.853 | | | | | | | | | | Lambda: 4.1268e-07 |
| 224 | 5 | Accept | 0.25187 | 1.6516 | 0.2399 | 0.24139 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0019286 | | | | | | | | | | KernelScale: 0.10433 |
| 225 | 5 | Accept | 0.25037 | 4.822 | 0.2399 | 0.24139 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 0.71239 | | | | | | | | | | Lambda: 0.015711 |
| 226 | 5 | Accept | 0.3452 | 10.241 | 0.2399 | 0.24139 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 0.14973 | | | | | | | | | | Lambda: 1.4037e-06 |
| 227 | 5 | Accept | 0.26204 | 9.4337 | 0.2399 | 0.24139 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 0.41479 | | | | | | | | | | Lambda: 3.5263e-06 |
| 228 | 5 | Accept | 0.25097 | 1.604 | 0.2399 | 0.24158 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.018254 | | | | | | | | | | KernelScale: 0.2875 |
| 229 | 5 | Accept | 0.36075 | 4.6692 | 0.2399 | 0.24158 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 24.105 | | | | | | | | | | Lambda: 3.803e-07 |
| 230 | 4 | Accept | 0.24559 | 465.69 | 0.2399 | 0.24158 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 318.11 | | | | | | | | | | KernelScale: 0.061933 | | 231 | 4 | Accept | 0.29016 | 4.7925 | 0.2399 | 0.24158 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 1.3656 | | | | | | | | | | Lambda: 0.035821 |
| 232 | 4 | Accept | 0.24649 | 1.5588 | 0.2399 | 0.24083 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.0013393 | | | | | | | | | | KernelScale: 0.068648 |
| 233 | 4 | Accept | 0.25426 | 1.4221 | 0.2399 | 0.24157 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.080107 | | | | | | | | | | KernelScale: 0.72702 |
| 234 | 4 | Accept | 0.31558 | 1.8074 | 0.2399 | 0.24166 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.067209 | | | | | | | | | | KernelScale: 2.6645 |
| 235 | 4 | Accept | 0.3443 | 3.9888 | 0.2399 | 0.24166 | kernel | Coding: onevsall | | | | | | | | | | KernelScale: 24.211 | | | | | | | | | | Lambda: 5.9888e-07 |
| 236 | 4 | Accept | 0.24619 | 3.3676 | 0.2399 | 0.24171 | svm | Coding: onevsone | | | | | | | | | | BoxConstraint: 0.64044 | | | | | | | | | | KernelScale: 0.072822 |
| 237 | 4 | Accept | 0.24948 | 4.0124 | 0.2399 | 0.24171 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 0.72883 | | | | | | | | | | Lambda: 0.020466 |
| 238 | 4 | Accept | 0.44601 | 3.6883 | 0.2399 | 0.2408 | svm | Coding: onevsall | | | | | | | | | | BoxConstraint: 0.016081 | | | | | | | | | | KernelScale: 0.065807 |
| 239 | 4 | Accept | 0.25636 | 4.1374 | 0.2399 | 0.2408 | kernel | Coding: onevsall | | | | | | | | | | KernelScale: 4.6533 | | | | | | | | | | Lambda: 9.4081e-07 |
| 240 | 4 | Accept | 0.26114 | 5.0408 | 0.2399 | 0.2408 | kernel | Coding: onevsone | | | | | | | | | | KernelScale: 12.554 | | | | | | | | | | Lambda: 1.5929e-06 |
__________________________________________________________ Optimization completed. MaxObjectiveEvaluations of 240 reached. Total function evaluations: 240 Total elapsed time: 1954.7195 seconds. Total objective function evaluation time: 2414.1608 Best observed feasible point is a multiclass svm model with: Coding (ECOC): onevsone BoxConstraint: 0.035844 KernelScale: 0.08166 Observed objective function value = 0.2399 Estimated objective function value = 0.24266 Function evaluation time = 1.4853 Best estimated feasible point (according to models) is a multiclass svm model with: Coding (ECOC): onevsone BoxConstraint: 0.018425 KernelScale: 0.05816 Estimated objective function value = 0.2408 Estimated function evaluation time = 1.6217
Итоговая модель возвращена fitcauto соответствует лучшей предполагаемой допустимой точке. Прежде, чем возвратить модель, функция переобучает его с помощью целых обучающих данных (XTrain и YTrain), перечисленный Learner (или модель) тип и отображенные гиперзначения параметров.
Оцените производительность набора тестов
Модель Mdl соответствует лучшей точке в Байесовой оптимизации согласно 'min-visited-mean' критерий. Чтобы измерить, как модель выполнит на новых данных, посмотрите на наблюдаемую точность перекрестной проверки модели (cvAccuracy) и его общая предполагаемая производительность на основе Байесовой оптимизации (estimatedAccuracy).
[x,~,iteration] = bestPoint(Results,'Criterion','min-visited-mean'); cvError = Results.ObjectiveTrace(iteration); cvAccuracy = 1 - cvError
cvAccuracy = 0.7601
estimatedError = predictObjective(Results,x); estimatedAccuracy = 1 - estimatedError
estimatedAccuracy = 0.7592
Оцените производительность модели на наборе тестов. Создайте матрицу беспорядка из результатов и задайте порядок классов в матрице беспорядка.
testAccuracy = 1 - loss(Mdl,XTest,YTest)
testAccuracy = 0.7438
cm = confusionchart(YTest,predict(Mdl,XTest));
sortClasses(cm,{'AAA','AA','A','BBB','BB','B','CCC'})
Входные параметры
Выходные аргументы
Советы
В зависимости от размера ваших данных и количества учеников вы задаете,
fitcautoможет занять время, чтобы запуститься. Если у вас есть лицензия Parallel Computing Toolbox, можно ускорить расчеты путем выполнения оптимизации параллельно. Для этого задайте'HyperparameterOptimizationOptions',struct('UseParallel',true). Можно включать другие поля в структуру, чтобы управлять другими аспектами оптимизации. СмотритеHyperparameterOptimizationOptions.
Алгоритмы
Альтернативная функциональность
Если вы не уверены, какие модели работают лучше всего на вас набор данных, можно альтернативно использовать приложение Classification Learner. Используя приложение, можно выполнить гиперпараметр, настраивающийся для различных моделей, и выбрать оптимизированную модель, которая выполняет лучше всего. Несмотря на то, что необходимо выбрать определенную модель, прежде чем можно будет настроить гиперпараметры модели, Classification Learner обеспечивает большую гибкость для выбора optimizable гиперпараметров и установки гиперзначений параметров. Однако вы не можете оптимизировать параллельно, выбрать
'linear'или'kernel'ученики, задайте веса наблюдения или задайте априорные вероятности в приложении. Для получения дополнительной информации смотрите Гипероптимизацию параметров управления в Приложении Classification Learner.Если вы знаете, какие модели могут удовлетворить вашим данным, можно альтернативно использовать соответствующие подходящие функции модели и задать
'OptimizeHyperparameters'аргумент пары "имя-значение", чтобы настроить гиперпараметры. Можно сравнить результаты через модели, чтобы выбрать лучший классификатор. Для примера этого процесса смотрите, что Двигающий Выбор Модели Автоматизации Использует Байесовую Оптимизацию.
Смотрите также
fitcdiscr | fitcecoc | fitcensemble | fitckernel | fitcknn | fitclinear | fitcnb | fitcsvm | fitctree