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

Example

R

Python

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]

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]