h2o.stackedEnsemble: Build a stacked ensemble (aka. Super Learner) using the H2O base
learning algorithms specified by the user.
Description
Build a stacked ensemble (aka. Super Learner) using the H2O base
learning algorithms specified by the user.
Usage
h2o.stackedEnsemble(x, y, training_frame, model_id = NULL,
validation_frame = NULL, base_models = list())
Arguments
x
A vector containing the names or indices of the predictor variables to use in building the model.
If x is missing,then all columns except y are used.
y
The name of the response variable in the model.If the data does not contain a header, this is the first column
index, and increasing from left to right. (The response must be either an integer or a
categorical variable).
training_frame
Id of the training data frame (Not required, to allow initial validation of model parameters).
model_id
Destination id for this model; auto-generated if not specified.
validation_frame
Id of the validation data frame.
base_models
List of model ids which we can stack together. Models must have been cross-validated using nfolds > 1, and
folds must be identical across models. Defaults to [].
Examples
Run this code# See example R code here:
# http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/stacked-ensembles.html
Run the code above in your browser using DataLab