public static final class NaiveBayesV3.NaiveBayesParametersV3 extends water.api.schemas3.ModelParametersSchemaV3<NaiveBayesModel.NaiveBayesParameters,NaiveBayesV3.NaiveBayesParametersV3>
Modifier and Type | Field and Description |
---|---|
boolean |
balance_classes
For imbalanced data, balance training data class counts via
over/under-sampling.
|
float[] |
class_sampling_factors
Desired over/under-sampling ratios per class (lexicographic order).
|
boolean |
compute_metrics |
double |
eps_prob |
double |
eps_sdev |
static java.lang.String[] |
fields |
double |
laplace |
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_hit_ratio_k
The maximum number (top K) of predictions to use for hit ratio computation (for multi-class only, 0 to disable)
|
double |
min_prob |
double |
min_sdev |
long |
seed |
categorical_encoding, checkpoint, distribution, fold_assignment, fold_column, huber_alpha, ignore_const_cols, ignored_columns, keep_cross_validation_fold_assignment, 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 |
---|
NaiveBayesV3.NaiveBayesParametersV3() |
append_field_arrays, fields, fillFromImpl, fillImpl, writeParametersJSON
createAndFillImpl, createImpl, extractVersionFromSchemaName, fillFromImpl, fillFromImpl, fillFromParms, fillFromParms, fillImpl, getImplClass, getImplClass, getSchemaName, getSchemaType, getSchemaVersion, init_meta, markdown, markdown, newInstance, newInstance, setField, setSchemaType_doNotCall
public static java.lang.String[] fields
@API(help="Balance training data class counts via over/under-sampling (for imbalanced data).", level=secondary, direction=INOUT) 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) 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) 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="Max. number (top K) of predictions to use for hit ratio computation (for multi-class only, 0 to disable)", level=secondary, direction=INOUT) public int max_hit_ratio_k
@API(help="Laplace smoothing parameter", gridable=true) public double laplace
@API(help="Min. standard deviation to use for observations with not enough data", gridable=true) public double min_sdev
@API(help="Cutoff below which standard deviation is replaced with min_sdev", gridable=true) public double eps_sdev
@API(help="Min. probability to use for observations with not enough data", gridable=true) public double min_prob
@API(help="Cutoff below which probability is replaced with min_prob", gridable=true) public double eps_prob
@API(help="Compute metrics on training data", gridable=true) public boolean compute_metrics
@API(help="Seed for pseudo random number generator (only used for cross-validation and fold_assignment=\"Random\" or \"AUTO\")", level=expert, direction=INOUT, gridable=true) public long seed