public static class SharedTreeV3.SharedTreeParametersV3<P extends SharedTreeModel.SharedTreeParameters,S extends SharedTreeV3.SharedTreeParametersV3<P,S>>
extends water.api.ModelParametersSchema<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 |
float[] |
class_sampling_factors
Desired over/under-sampling ratios per class (lexicographic order).
|
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 |
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_rows |
int |
nbins |
int |
nbins_cats |
int |
nbins_top_level |
int |
ntrees |
double |
r2_stopping |
long |
seed |
| Constructor and Description |
|---|
SharedTreeV3.SharedTreeParametersV3() |
append_field_arrays, fields, fillFromImpl, fillImpl, writeParametersJSONcreateAndFillImpl, createImpl, fillFromParms, fillFromParms, get__meta, getExperimentalVersion, getHighestSupportedVersion, getImplClass, getImplClass, getLatestVersion, getSchemaVersion, markdown, markdown, markdown, newInstance, newInstance, registerAllSchemasIfNecessary, schema, schema, schemaClass, schemas, setField@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="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,
gridable=true)
public int max_hit_ratio_k
@API(help="Number of trees.",
gridable=true)
public int ntrees
@API(help="Maximum tree depth.",
gridable=true)
public int max_depth
@API(help="Fewest allowed (weighted) observations in a leaf (in R called \'nodesize\').",
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=expert,
gridable=true)
public int nbins_top_level
@API(help="For categorical columns (enum), build a histogram of this many bins, then split at the best point. Higher values can lead to more overfitting.",
gridable=true)
public int nbins_cats
@API(help="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=secondary)
public boolean build_tree_one_node