base_models¶
Available in: Stacked Ensembles
Hyperparameter: no
Description¶
H2O’s Stacked Ensemble method is a supervised ensemble machine learning algorithm that finds the optimal combination of a collection of prediction algorithms using a process called stacking (or Super Learning). The algorithm that learns the optimal combination of the base learners is called the metalearning algorithm or metalearner.
The base_models parameter is used to specify a list of models (or model IDs) that can be stacked together. Models must have been cross-validated (i.e., nfolds>1 or fold_column was specified), they all must use the same cross-validation folds, and keep_cross_validation_predictions must have been set to True. One way to guarantee identical folds across base models is to set fold_assignment = "Modulo" in all the base models. It is also possible to get identical folds by setting fold_assignment = "Random" when the same seed is used in all base models.
Example¶
library(h2o)
h2o.init()
# import the higgs_train_5k train and test datasets
train <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv")
test <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_test_5k.csv")
# Identify predictors and response
y <- "response"
x <- setdiff(names(train), y)
# Convert the response column in train and test datasets to a factor
train[,y] <- as.factor(train[,y])
test[,y] <- as.factor(test[,y])
# Set number of folds for base learners
nfolds <- 3
# Train & Cross-validate a GBM model
my_gbm <- h2o.gbm(x = x,
y = y,
training_frame = train,
distribution = "bernoulli",
ntrees = 10,
nfolds = nfolds,
keep_cross_validation_predictions = TRUE,
seed = 1)
# Train & Cross-validate an RF model
my_rf <- h2o.randomForest(x = x,
y = y,
training_frame = train,
ntrees = 10,
nfolds = nfolds,
keep_cross_validation_predictions = TRUE,
seed = 1)
# Next we can train a few different ensembles using different metalearners
# Train a stacked ensemble using the default metalearner algorithm
stack <- h2o.stackedEnsemble(x = x,
y = y,
training_frame = train,
base_models = list(my_gbm, my_rf))
h2o.auc(h2o.performance(stack, test))
# 0.7570171
# Train a stacked ensemble using GBM as the metalearner algorithm
# The metalearner will use GBM default values
stack_gbm <- h2o.stackedEnsemble(x = x,
y = y,
training_frame = train,
base_models = list(my_gbm, my_rf),
metalearner_algorithm = "gbm")
h2o.auc(h2o.performance(stack_gbm, test))
# 0.7511055
# Train a stacked ensemble using RF as the metalearner algorithm
# The metelearner will use RF default values
stack_rf <- h2o.stackedEnsemble(x = x,
y = y,
training_frame = train,
base_models = list(my_gbm, my_rf),
metalearner_algorithm = "drf")
h2o.auc(h2o.performance(stack_rf, test))
# 0.7232461
# Train a stacked ensemble using Deep Learning as the metalearner algorithm
# The metelearner will use RF default values
stack_dl <- h2o.stackedEnsemble(x = x,
y = y,
training_frame = train,
base_models = list(my_gbm, my_rf),
metalearner_algorithm = "deeplearning")
h2o.auc(h2o.performance(stack_dl, test))
# 0.7571556
import h2o
from h2o.estimators.random_forest import H2ORandomForestEstimator
from h2o.estimators.gbm import H2OGradientBoostingEstimator
from h2o.estimators.stackedensemble import H2OStackedEnsembleEstimator
h2o.init()
# import the higgs_train_5k train and test datasets
train = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv")
test = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_test_5k.csv")
# Identify predictors and response
x = train.columns
y = "response"
x.remove(y)
# Convert the response column in train and test datasets to a factor
train[y] = train[y].asfactor()
test[y] = test[y].asfactor()
# Set number of folds for base learners
nfolds = 3
# Train and cross-validate a GBM model
my_gbm = H2OGradientBoostingEstimator(distribution="bernoulli",
ntrees=10,
nfolds=nfolds,
fold_assignment="Modulo",
keep_cross_validation_predictions=True,
seed=1)
my_gbm.train(x=x, y=y, training_frame=train)
# Train and cross-validate an RF model
my_rf = H2ORandomForestEstimator(ntrees=50,
nfolds=nfolds,
fold_assignment="Modulo",
keep_cross_validation_predictions=True,
seed=1)
my_rf.train(x=x, y=y, training_frame=train)
# Next we can train a few different ensembles using different metalearners
# Train a stacked ensemble using the default metalearner algorithm
stack = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf])
stack.train(x=x, y=y, training_frame=train)
stack.model_performance(test).auc()
# 0.7522591310013634
# Train a stacked ensemble with a GBM metalearner algorithm
# The metelearner will use GBM default values
stack_gbm = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf],
metalearner_algorithm="gbm")
stack_gbm.train(x=x, y=y, training_frame=train)
stack_gbm.model_performance(test).auc()
# 0.7522591310013634
# Train a stacked ensemble with a RF metalearner algorithm
# The metelearner will use RF default values
stack_rf = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf],
metalearner_algorithm="drf")
stack_rf.train(x=x, y=y, training_frame=train)
stack_rf.model_performance(test).auc()
# 0.7016302070136065
# Train a stacked ensemble with a Deep Learning metalearner algorithm
# The metelearner will use Deep Learning default values
stack_dl = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf],
metalearner_algorithm="deeplearning")
stack_dl.train(x=x, y=y, training_frame=train)
stack_dl.model_performance(test).auc()
# 0.7634122856763638