H2O Segmented-Data Bulk Model Training — h2o.train

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

Usage

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, anovaglm, modelselection).
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

if (FALSE) { # \dontrun{
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)
} # }