include_algos

• Available in: AutoML

• Hyperparameter: no

Description

This option allows you to specify a list of algorithms to include in an AutoML run during the model-building phase. This option defaults to None/Null, which means that all algorithms are included unless any algorithms are specified in the exclude_algos option. 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

• exclude_algos

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 using only GLM, DeepLearning, and DRF
aml <- h2o.automl(x = x, y = y,
                  training_frame = train,
                  max_runtime_secs = 30,
                  sort_metric = "logloss",
                  include_algos = c("GLM", "DeepLearning", "DRF"))

# View the AutoML Leaderboard
lb <- aml@leaderboard
lb

                                            model_id       auc   logloss
1                       XRT_1_AutoML_20190321_094944 0.7402090 0.6051397
2                       DRF_1_AutoML_20190321_094944 0.7431221 0.6057202
3              DeepLearning_1_AutoML_20190321_094944 0.6994255 0.6309644
4          GLM_grid_1_AutoML_20190321_094944_model_1 0.6826481 0.6385205
5 DeepLearning_grid_1_AutoML_20190321_094944_model_1 0.6707953 0.7042976
  mean_per_class_error      rmse       mse
1            0.3545519 0.4539312 0.2060535
2            0.3683363 0.4527405 0.2049739
3            0.3892368 0.4687153 0.2196940
4            0.3972341 0.4726827 0.2234290
5            0.4385448 0.4911634 0.2412415

[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 using only GLM, DeepLearning, and DRF
aml = H2OAutoML(max_runtime_secs = 30, sort_metric = "logloss",
                include_algos = ["GLM", "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
--------------------------------------------------  --------  ---------  ----------------------  --------  --------
XRT_1_AutoML_20190321_095341                        0.741603   0.60012                 0.342847  0.453342  0.205519
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
GLM_grid_1_AutoML_20190321_095341_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

[5 rows x 6 columns]