``exclude_algos`` ----------------- - Available in: AutoML - Hyperparameter: no Description ~~~~~~~~~~~ This option allows you to specify a list of algorithms that should not be included in an AutoML run during the model-building phase. This option defaults to None/Null, which means that all algorithms are included. However, if the ``include_algos`` option is used, then the AutoML run will include only those specified algorithms. Note that these two options cannot both be specified. The algorithms that can be specified include: - ``DRF`` (including both the Random Forest and Extremely Randomized Trees (XRT) models) - ``GLM`` - ``XGBoost`` (XGBoost GBM) - ``GBM`` (H2O GBM) - ``DeepLearning`` (Fully-connected multi-layer artificial neural network) - ``StackedEnsemble`` Related Parameters ~~~~~~~~~~~~~~~~~~ - `include_algos `__ Example ~~~~~~~ .. tabs:: .. code-tab:: r R library(h2o) h2o.init() # Import a sample binary outcome training set into H2O train <- h2o.importFile("https://s3.amazonaws.com/erin-data/higgs/higgs_train_10k.csv") # Identify predictors and response x <- setdiff(names(train), y) y <- "response" # For binary classification, response should be a factor train[, y] <- as.factor(train[, y]) # Train AutoML, omitting DeepLearning and DRF aml <- h2o.automl(x = x, y = y, training_frame = train, max_runtime_secs = 30, sort_metric = "logloss", exclude_algos = c("DeepLearning", "DRF")) # View the AutoML Leaderboard lb <- aml@leaderboard lb model_id auc logloss 1 StackedEnsemble_AllModels_AutoML_20190321_095825 0.7866967 0.5550255 2 StackedEnsemble_BestOfFamily_AutoML_20190321_095825 0.7848515 0.5569458 3 XGBoost_1_AutoML_20190321_095825 0.7846668 0.5578654 4 XGBoost_2_AutoML_20190321_095825 0.7820392 0.5586830 5 GLM_grid_1_AutoML_20190321_095825_model_1 0.6826481 0.6385205 mean_per_class_error rmse mse 1 0.3309041 0.4338530 0.1882284 2 0.3231440 0.4346720 0.1889397 3 0.3324049 0.4349659 0.1891953 4 0.3269806 0.4356756 0.1898132 5 0.3972341 0.4726827 0.2234290 [5 rows x 6 columns] .. code-tab:: python import h2o from h2o.automl import H2OAutoML h2o.init() # Import a sample binary outcome training set into H2O train = h2o.import_file("https://s3.amazonaws.com/erin-data/higgs/higgs_train_10k.csv") # Identify predictors and response x = train.columns y = "response" x.remove(y) # For binary classification, response should be a factor train[y] = train[y].asfactor() # Train AutoML, omitting DeepLearning and DRF aml = H2OAutoML(max_runtime_secs = 30, sort_metric = "logloss", exclude_algos = ["DeepLearning", "DRF"]) aml.train(x = x, y = y, training_frame = train) # View the AutoML Leaderboard lb = aml.leaderboard lb model_id auc logloss mean_per_class_error rmse mse -------------------------------------------------- -------- --------- ---------------------- -------- -------- DRF_1_AutoML_20190321_100107 0.744882 0.597348 0.360293 0.452093 0.204388 XRT_1_AutoML_20190321_095341 0.741603 0.60012 0.342847 0.453342 0.205519 XRT_1_AutoML_20190321_100107 0.740636 0.600695 0.356075 0.453646 0.205795 DRF_1_AutoML_20190321_095341 0.740674 0.60294 0.375423 0.453271 0.205454 DeepLearning_grid_1_AutoML_20190321_095341_model_1 0.711473 0.620394 0.387857 0.463987 0.215284 DeepLearning_1_AutoML_20190321_100107 0.703753 0.628472 0.401192 0.467294 0.218363 GLM_grid_1_AutoML_20190321_095341_model_1 0.682648 0.63852 0.397234 0.472683 0.223429 GLM_grid_1_AutoML_20190321_100107_model_1 0.682648 0.63852 0.397234 0.472683 0.223429 DeepLearning_1_AutoML_20190321_095341 0.684733 0.639195 0.418683 0.472425 0.223185 DeepLearning_grid_1_AutoML_20190321_100107_model_1 0.670713 0.643133 0.434458 0.475507 0.226107 [10 rows x 6 columns]