public static class SharedTreeV3.SharedTreeParametersV3<P extends SharedTreeModel.SharedTreeParameters,S extends SharedTreeV3.SharedTreeParametersV3<P,S>>
extends water.api.schemas3.ModelParametersSchemaV3<P,S>
| Modifier and Type | Field and Description |
|---|---|
boolean |
balance_classes
For imbalanced data, balance training data class counts via
over/under-sampling.
|
boolean |
build_tree_one_node |
boolean |
calibrate_model |
water.api.schemas3.KeyV3.FrameKeyV3 |
calibration_frame |
CalibrationHelper.CalibrationMethod |
calibration_method |
boolean |
check_constant_response |
float[] |
class_sampling_factors
Desired over/under-sampling ratios per class (lexicographic order).
|
double |
col_sample_rate_change_per_level |
double |
col_sample_rate_per_tree |
SharedTreeModel.SharedTreeParameters.HistogramType |
histogram_type |
java.lang.String |
in_training_checkpoints_dir |
int |
in_training_checkpoints_tree_interval |
float |
max_after_balance_size
When classes are balanced, limit the resulting dataset size to the
specified multiple of the original dataset size.
|
int |
max_confusion_matrix_size
For classification models, the maximum size (in terms of classes) of
the confusion matrix for it to be printed.
|
int |
max_depth |
double |
min_rows |
double |
min_split_improvement |
int |
nbins |
int |
nbins_cats |
int |
nbins_top_level |
int |
ntrees |
double |
r2_stopping |
double[] |
sample_rate_per_class |
int |
score_tree_interval |
long |
seed |
auc_type, categorical_encoding, checkpoint, custom_distribution_func, custom_metric_func, distribution, export_checkpoints_dir, fold_assignment, fold_column, gainslift_bins, 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 |
|---|
SharedTreeParametersV3() |
append_field_arrays, extractDeclaredApiParameters, fields, fillFromImpl, fillImpl, getAdditionalParameters, writeParametersJSONcreateAndFillImpl, createImpl, extractVersionFromSchemaName, fillFromAny, fillFromBody, fillFromImpl, fillFromImpl, fillFromParms, fillFromParms, fillFromParms, fillImpl, getImplClass, getImplClass, getSchemaName, getSchemaType, getSchemaVersion, init_meta, markdown, markdown, newInstance, newInstance, setField, setSchemaType_doNotCall@API(help="Balance training data class counts via over/under-sampling (for imbalanced data).",
level=secondary,
direction=INOUT,
gridable=true)
public boolean balance_classes
@API(help="Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.",
level=expert,
direction=INOUT,
gridable=true)
public float[] class_sampling_factors
@API(help="Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes.",
level=expert,
direction=INOUT,
gridable=true)
public float max_after_balance_size
@API(help="[Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs",
level=secondary,
direction=INOUT)
public int max_confusion_matrix_size
@API(help="Number of trees.",
gridable=true)
public int ntrees
@API(help="Maximum tree depth (0 for unlimited).",
gridable=true)
public int max_depth
@API(help="Fewest allowed (weighted) observations in a leaf.",
gridable=true)
public double min_rows
@API(help="For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point",
gridable=true)
public int nbins
@API(help="For numerical columns (real/int), build a histogram of (at most) this many bins at the root level, then decrease by factor of two per level",
level=secondary,
gridable=true)
public int nbins_top_level
@API(help="For categorical columns (factors), build a histogram of this many bins, then split at the best point. Higher values can lead to more overfitting.",
level=secondary,
gridable=true)
public int nbins_cats
@API(help="r2_stopping is no longer supported and will be ignored if set - please use stopping_rounds, stopping_metric and stopping_tolerance instead. Previous version of H2O would stop making trees when the R^2 metric equals or exceeds this",
level=secondary,
gridable=true)
public double r2_stopping
@API(help="Seed for pseudo random number generator (if applicable)",
gridable=true)
public long seed
@API(help="Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets.",
level=expert,
gridable=false)
public boolean build_tree_one_node
@API(help="A list of row sample rates per class (relative fraction for each class, from 0.0 to 1.0), for each tree",
level=expert,
gridable=true)
public double[] sample_rate_per_class
@API(help="Column sample rate per tree (from 0.0 to 1.0)",
level=secondary,
gridable=true)
public double col_sample_rate_per_tree
@API(help="Relative change of the column sampling rate for every level (must be > 0.0 and <= 2.0)",
level=expert,
gridable=true)
public double col_sample_rate_change_per_level
@API(help="Score the model after every so many trees. Disabled if set to 0.",
level=secondary,
gridable=false)
public int score_tree_interval
@API(help="Minimum relative improvement in squared error reduction for a split to happen",
level=secondary,
gridable=true)
public double min_split_improvement
@API(help="What type of histogram to use for finding optimal split points",
values={"AUTO","UniformAdaptive","Random","QuantilesGlobal","RoundRobin","UniformRobust"},
level=secondary,
gridable=true)
public SharedTreeModel.SharedTreeParameters.HistogramType histogram_type
@API(help="Use Platt Scaling (default) or Isotonic Regression to calculate calibrated class probabilities. Calibration can provide more accurate estimates of class probabilities.",
level=expert)
public boolean calibrate_model
@API(help="Data for model calibration",
level=expert,
direction=INOUT)
public water.api.schemas3.KeyV3.FrameKeyV3 calibration_frame
@API(help="Calibration method to use",
values={"AUTO","PlattScaling","IsotonicRegression"},
level=expert,
direction=INOUT)
public CalibrationHelper.CalibrationMethod calibration_method
@API(help="Check if response column is constant. If enabled, then an exception is thrown if the response column is a constant value.If disabled, then model will train regardless of the response column being a constant value or not.",
level=expert,
direction=INOUT)
public boolean check_constant_response
@API(help="Create checkpoints into defined directory while training process is still running. In case of cluster shutdown, this checkpoint can be used to restart training.",
level=expert,
gridable=false)
public java.lang.String in_training_checkpoints_dir
@API(help="Checkpoint the model after every so many trees. Parameter is used only when in_training_checkpoints_dir is defined",
level=expert,
gridable=false)
public int in_training_checkpoints_tree_interval