package hex.schemas;
import hex.tree.drf.DRF;
import hex.tree.drf.DRFModel.DRFParameters;
import water.api.API;
public class DRFV3 extends SharedTreeV3<DRF,DRFV3, DRFV3.DRFParametersV3> {
public static final class DRFParametersV3 extends SharedTreeV3.SharedTreeParametersV3<DRFParameters, DRFParametersV3> {
static public String[] fields = new String[] {
"model_id",
"training_frame",
"validation_frame",
"nfolds",
"keep_cross_validation_predictions",
"keep_cross_validation_fold_assignment",
"score_each_iteration",
"score_tree_interval",
"fold_assignment",
"fold_column",
"response_column",
"ignored_columns",
"ignore_const_cols",
"offset_column",
"weights_column",
"balance_classes",
"class_sampling_factors",
"max_after_balance_size",
"max_confusion_matrix_size",
"max_hit_ratio_k",
"ntrees",
"max_depth",
"min_rows",
"nbins",
"nbins_top_level",
"nbins_cats",
"r2_stopping",
"stopping_rounds",
"stopping_metric",
"stopping_tolerance",
"max_runtime_secs",
"seed",
"build_tree_one_node",
"mtries",
"sample_rate",
"sample_rate_per_class",
"binomial_double_trees",
"checkpoint",
"col_sample_rate_change_per_level",
"col_sample_rate_per_tree",
"min_split_improvement",
"histogram_type",
"categorical_encoding",
"calibrate_model",
"calibration_frame"
};
// Input fields
@API(help = "Number of variables randomly sampled as candidates at each split. If set to -1, defaults to sqrt{p} for classification and p/3 for regression (where p is the # of predictors", gridable = true)
public int mtries;
@API(help="For binary classification: Build 2x as many trees (one per class) - can lead to higher accuracy.", level = API.Level.expert)
public boolean binomial_double_trees;
}
}