public static final class StackedEnsembleV99.StackedEnsembleParametersV99 extends water.api.schemas3.ModelParametersSchemaV3<StackedEnsembleModel.StackedEnsembleParameters,StackedEnsembleV99.StackedEnsembleParametersV99>
| Modifier and Type | Field and Description |
|---|---|
water.api.schemas3.KeyV3.ModelKeyV3[] |
base_models |
water.api.schemas3.KeyV3.FrameKeyV3 |
blending_frame |
static java.lang.String[] |
fields |
boolean |
keep_levelone_frame |
Metalearner.Algorithm |
metalearner_algorithm |
hex.Model.Parameters.FoldAssignmentScheme |
metalearner_fold_assignment |
water.api.schemas3.FrameV3.ColSpecifierV3 |
metalearner_fold_column |
int |
metalearner_nfolds |
java.lang.String |
metalearner_params |
long |
seed |
categorical_encoding, checkpoint, custom_distribution_func, custom_metric_func, distribution, export_checkpoints_dir, fold_assignment, fold_column, huber_alpha, ignore_const_cols, ignored_columns, keep_cross_validation_fold_assignment, keep_cross_validation_models, keep_cross_validation_predictions, max_categorical_levels, max_runtime_secs, model_id, nfolds, offset_column, parallelize_cross_validation, quantile_alpha, response_column, score_each_iteration, stopping_metric, stopping_rounds, stopping_tolerance, training_frame, tweedie_power, validation_frame, weights_column| Constructor and Description |
|---|
StackedEnsembleParametersV99() |
| Modifier and Type | Method and Description |
|---|---|
StackedEnsembleV99.StackedEnsembleParametersV99 |
fillFromImpl(StackedEnsembleModel.StackedEnsembleParameters impl) |
StackedEnsembleModel.StackedEnsembleParameters |
fillImpl(StackedEnsembleModel.StackedEnsembleParameters impl) |
append_field_arrays, fields, writeParametersJSONcreateAndFillImpl, createImpl, extractVersionFromSchemaName, fillFromBody, fillFromImpl, fillFromImpl, fillFromParms, fillFromParms, fillImpl, getImplClass, getImplClass, getSchemaName, getSchemaType, getSchemaVersion, init_meta, markdown, markdown, newInstance, newInstance, setField, setSchemaType_doNotCallpublic static java.lang.String[] fields
@API(level=critical,
direction=INOUT,
help="List of models (or model ids) to ensemble/stack together. If not using blending frame, then models must have been cross-validated using nfolds > 1, and folds must be identical across models.",
required=true)
public water.api.schemas3.KeyV3.ModelKeyV3[] base_models
@API(level=critical,
direction=INOUT,
values={"AUTO","glm","gbm","drf","deeplearning"},
help="Type of algorithm to use as the metalearner. Options include \'AUTO\' (GLM with non negative weights; if validation_frame is present, a lambda search is performed), \'glm\' (GLM with default parameters), \'gbm\' (GBM with default parameters), \'drf\' (Random Forest with default parameters), or \'deeplearning\' (Deep Learning with default parameters).")
public Metalearner.Algorithm metalearner_algorithm
@API(level=critical,
direction=INOUT,
help="Number of folds for K-fold cross-validation of the metalearner algorithm (0 to disable or >= 2).")
public int metalearner_nfolds
@API(level=secondary,
direction=INOUT,
values={"AUTO","Random","Modulo","Stratified"},
help="Cross-validation fold assignment scheme for metalearner cross-validation. Defaults to AUTO (which is currently set to Random). The \'Stratified\' option will stratify the folds based on the response variable, for classification problems.")
public hex.Model.Parameters.FoldAssignmentScheme metalearner_fold_assignment
@API(level=secondary,
direction=INOUT,
is_member_of_frames="training_frame",
is_mutually_exclusive_with={"ignored_columns","response_column"},
help="Column with cross-validation fold index assignment per observation for cross-validation of the metalearner.")
public water.api.schemas3.FrameV3.ColSpecifierV3 metalearner_fold_column
@API(level=secondary,
help="Keep level one frame used for metalearner training.")
public boolean keep_levelone_frame
@API(help="Parameters for metalearner algorithm",
direction=INOUT)
public java.lang.String metalearner_params
@API(help="Frame used to compute the predictions that serve as the training frame for the metalearner (triggers blending mode if provided)",
direction=INOUT)
public water.api.schemas3.KeyV3.FrameKeyV3 blending_frame
@API(help="Seed for random numbers; passed through to the metalearner algorithm. Defaults to -1 (time-based random number)",
gridable=true)
public long seed
public StackedEnsembleV99.StackedEnsembleParametersV99 fillFromImpl(StackedEnsembleModel.StackedEnsembleParameters impl)
fillFromImpl in class water.api.schemas3.ModelParametersSchemaV3<StackedEnsembleModel.StackedEnsembleParameters,StackedEnsembleV99.StackedEnsembleParametersV99>public StackedEnsembleModel.StackedEnsembleParameters fillImpl(StackedEnsembleModel.StackedEnsembleParameters impl)
fillImpl in class water.api.schemas3.ModelParametersSchemaV3<StackedEnsembleModel.StackedEnsembleParameters,StackedEnsembleV99.StackedEnsembleParametersV99>