org.apache.spark.ml.tree

Interface DecisionTreeParams

All Superinterfaces:

HasCheckpointInterval, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasSeed, HasWeightCol, Identifiable, Params, PredictorParams, java.io.Serializable

All Known Subinterfaces:

DecisionTreeClassifierParams, DecisionTreeRegressorParams, GBTClassifierParams, GBTParams, GBTRegressorParams, RandomForestClassifierParams, RandomForestParams, RandomForestRegressorParams, TreeEnsembleClassifierParams, TreeEnsembleParams, TreeEnsembleRegressorParams

All Known Implementing Classes:

DecisionTreeClassificationModel, DecisionTreeClassifier, DecisionTreeRegressionModel, DecisionTreeRegressor, GBTClassificationModel, GBTClassifier, GBTRegressionModel, GBTRegressor, RandomForestClassificationModel, RandomForestClassifier, RandomForestRegressionModel, RandomForestRegressor

public interface DecisionTreeParams
extends PredictorParams, HasCheckpointInterval, HasSeed, HasWeightCol

Parameters for Decision Tree-based algorithms.

Note: Marked as private since this may be made public in the future.

Method Summary

Modifier and Type	Method and Description
BooleanParam	cacheNodeIds() If false, the algorithm will pass trees to executors to match instances with nodes.
boolean	getCacheNodeIds()
String	getLeafCol()
int	getMaxBins()
int	getMaxDepth()
int	getMaxMemoryInMB()
double	getMinInfoGain()
int	getMinInstancesPerNode()
double	getMinWeightFractionPerNode()
Strategy	getOldStrategy(scala.collection.immutable.Map<Object,Object> categoricalFeatures, int numClasses, scala.Enumeration.Value oldAlgo, Impurity oldImpurity, double subsamplingRate) (private[ml]) Create a Strategy instance to use with the old API.
Param<String>	leafCol() Leaf indices column name.
IntParam	maxBins() Maximum number of bins used for discretizing continuous features and for choosing how to split on features at each node.
IntParam	maxDepth() Maximum depth of the tree (nonnegative).
IntParam	maxMemoryInMB() Maximum memory in MB allocated to histogram aggregation.
DoubleParam	minInfoGain() Minimum information gain for a split to be considered at a tree node.
IntParam	minInstancesPerNode() Minimum number of instances each child must have after split.
DoubleParam	minWeightFractionPerNode() Minimum fraction of the weighted sample count that each child must have after split.
DecisionTreeParams	setLeafCol(String value)

Methods inherited from interface org.apache.spark.ml.PredictorParams

validateAndTransformSchema

Methods inherited from interface org.apache.spark.ml.param.shared.HasLabelCol

getLabelCol, labelCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasFeaturesCol

featuresCol, getFeaturesCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasPredictionCol

getPredictionCol, predictionCol

Methods inherited from interface org.apache.spark.ml.param.Params

clear, copy, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn

Methods inherited from interface org.apache.spark.ml.util.Identifiable

toString, uid

Methods inherited from interface org.apache.spark.ml.param.shared.HasCheckpointInterval

checkpointInterval, getCheckpointInterval

Methods inherited from interface org.apache.spark.ml.param.shared.HasSeed

getSeed, seed

Methods inherited from interface org.apache.spark.ml.param.shared.HasWeightCol

getWeightCol, weightCol

Method Detail

cacheNodeIds

BooleanParam cacheNodeIds()

If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees. Users can set how often should the cache be checkpointed or disable it by setting checkpointInterval. (default = false)

Returns:

(undocumented)

getCacheNodeIds

boolean getCacheNodeIds()

getLeafCol

String getLeafCol()

getMaxBins

int getMaxBins()

getMaxDepth

int getMaxDepth()

getMaxMemoryInMB

int getMaxMemoryInMB()

getMinInfoGain

double getMinInfoGain()

getMinInstancesPerNode

int getMinInstancesPerNode()

getMinWeightFractionPerNode

double getMinWeightFractionPerNode()

getOldStrategy

Strategy getOldStrategy(scala.collection.immutable.Map<Object,Object> categoricalFeatures,
                        int numClasses,
                        scala.Enumeration.Value oldAlgo,
                        Impurity oldImpurity,
                        double subsamplingRate)

(private[ml]) Create a Strategy instance to use with the old API.

leafCol

Param<String> leafCol()

Leaf indices column name. Predicted leaf index of each instance in each tree by preorder. (default = "")

Returns:

(undocumented)

maxBins

IntParam maxBins()

Maximum number of bins used for discretizing continuous features and for choosing how to split on features at each node. More bins give higher granularity. Must be at least 2 and at least number of categories in any categorical feature. (default = 32)

Returns:

(undocumented)

maxDepth

IntParam maxDepth()

Maximum depth of the tree (nonnegative). E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes. (default = 5)

Returns:

(undocumented)

maxMemoryInMB

IntParam maxMemoryInMB()

Maximum memory in MB allocated to histogram aggregation. If too small, then 1 node will be split per iteration, and its aggregates may exceed this size. (default = 256 MB)

Returns:

(undocumented)

minInfoGain

DoubleParam minInfoGain()

Minimum information gain for a split to be considered at a tree node. Should be at least 0.0. (default = 0.0)

Returns:

(undocumented)

minInstancesPerNode

IntParam minInstancesPerNode()

Minimum number of instances each child must have after split. If a split causes the left or right child to have fewer than minInstancesPerNode, the split will be discarded as invalid. Must be at least 1. (default = 1)

Returns:

(undocumented)

minWeightFractionPerNode

DoubleParam minWeightFractionPerNode()

Minimum fraction of the weighted sample count that each child must have after split. If a split causes the fraction of the total weight in the left or right child to be less than minWeightFractionPerNode, the split will be discarded as invalid. Should be in the interval [0.0, 0.5). (default = 0.0)

Returns:

(undocumented)

setLeafCol

DecisionTreeParams setLeafCol(String value)