blending_frame

  • Available in: Stacked Ensembles, AutoML

  • Hyperparameter: no

Description

H2O’s Stacked Ensemble method is 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 optional blending_frame parameter is used to specify a frame to be used for computing the predictions that serve as the training frame for the metalearner. If provided, this triggers blending mode. Blending mode is faster than cross-validating the base learners (though these ensembles may not perform as well as the Super Learner ensemble). In addition, a blending frame adds the ability to train stacked ensembles on time-series data, where holdout data is “future” data compared to “past” data in training set.

Example

R

Python

library(h2o)
h2o.init()

# import the higgs_train_5k train and test datasets
higgs <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv")

# split the dataset into training and blending frames
higgs_splits <- h2o.splitFrame(data =  higgs, ratios = 0.8, seed = 1234)
train <- higgs_splits[[1]]
blend <- higgs_splits[[2]]

# 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])
blend[, y] <- as.factor(blend[, 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)

# Train a stacked ensemble using a blending frame
stack <- h2o.stackedEnsemble(x = x,
                             y = y,
                             base_models = list(my_gbm, my_rf),
                             training_frame = train,
                             blending_frame = blend,
                             seed = 1)
h2o.auc(h2o.performance(stack, blend))
# [1] 0.7576039

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
higgs = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/testng/higgs_train_5k.csv")

# split the dataset into training and blending
train, blend = higgs.split_frame(ratios = [.8], seed = 1234)

# 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()
blend[y] = blend[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)

# Train a stacked ensemble using a blending frame
stack_blend = H2OStackedEnsembleEstimator(base_models=[my_gbm, my_rf],
                                          seed=1)
stack_blend.train(x=x, y=y, training_frame=train, blending_frame=blend)
stack_blend.model_performance(blend).auc()
# 0.7736312597328088