H2O Segmented-Data Bulk Model Training

Provides a set of functions to train a group of models on different segments (subpopulations) of the training set.

h2o.train_segments(
  algorithm,
  segment_columns,
  segment_models_id,
  parallelism = 1,
  ...
)

Arguments

algorithm	Name of algorithm to use in training segment models (gbm, randomForest, kmeans, glm, deeplearning, naivebayes, psvm, xgboost, pca, svd, targetencoder, aggregator, word2vec, coxph, isolationforest, kmeans, stackedensemble, glrm, gam).
segment_columns	A list of columns to segment-by. H2O will group the training (and validation) dataset by the segment-by columns and train a separate model for each segment (group of rows).
segment_models_id	Identifier for the returned collection of Segment Models. If not specified it will be automatically generated.
parallelism	Level of parallelism of bulk model building, it is the maximum number of models each H2O node will be building in parallel, defaults to 1.
...	Use to pass along training_frame parameter, x, y, and all non-default parameter values to the algorithm Look at the specific algorithm - h2o.gbm, h2o.glm, h2o.kmeans, h2o.deepLearning - for available parameters.

Details

Start Segmented-Data bulk Model Training for a given algorithm and parameters.

Examples

# NOT RUN {
library(h2o)
h2o.init()
iris_hf <- as.h2o(iris)
models <- h2o.train_segments(algorithm = "gbm",
                             segment_columns = "Species",
                             x = c(1:3), y = 4,
                             training_frame = iris_hf,
                             ntrees = 5,
                             max_depth = 4)
as.data.frame(models)
# }