Keep_cross_validation_fold_assignment: Enable this option to preserve the cross-validation fold assignment. Keep_cross_validation_predictions: Specify whether to keep the cross-validation predictions. Keeping cross-validation models may consume significantly more memory in the H2O cluster. Keep_cross_validation_models: Specify whether to keep the cross-validated models. The solver option must be set explicitly to IRLSM and cannot be set to AUTO or DEFAULT. In addition, checkpoint currently does not work when cross-validation is enabled. Note: GLM only supports checkpoint for the IRLSM solver. Use this option to build a new model as a continuation of a previously generated model. If x is missing, then all columns except y are used.Ĭheckpoint: Enter a model key associated with a previously trained model. X: Specify a vector containing the names or indices of the predictor variables to use when building the model. If the family is Binomial, the dataset cannot contain more than two levels. Y: (Required) Specify the column to use as the dependent variable.įor a regression model, this column must be numeric ( Real or Int).įor a classification model, this column must be categorical ( Enum or String).
This option defaults to -1 (time-based random number). The seed is consistent for each H2O instance so that you can create models with the same starting conditions in alternative configurations. Seed: Specify the random number generator (RNG) seed for algorithm components dependent on randomization. Nfolds: Specify the number of folds for cross-validation. Validation_frame: (Optional) Specify the dataset used to evaluate Parse cell, the training frame is entered automatically. NOTE: In Flow, if you click the Build a model button from the Training_frame: (Required) Specify the dataset used to build the By default, H2O automatically generates a destination Model_id: (Optional) Specify a custom name for the model to use asĪ reference.
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