com.workfusion.vds.sdk.ml.sklearn.extratrees

Class ExtraTreesClassifier

java.lang.Object

com.workfusion.vds.sdk.ml.python.BasePythonClassifier

com.workfusion.vds.sdk.ml.sklearn.extratrees.ExtraTreesClassifier

All Implemented Interfaces:

Classifier, PythonClassifier, AutoCloseable

public class ExtraTreesClassifier
extends BasePythonClassifier

The type Random forest classifier.

Since:

10.0

Author:

Konstantin Kovalev

See Also:

ExtraTreesClassifier

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	ExtraTreesClassifier.Builder The type Builder.

Method Summary

Modifier and Type	Method and Description
static ExtraTreesClassifier.Builder	builder() Construct new builder instance.
ClassWeightType	getClassWeight() Gets class weight.
CriterionType	getCriterion() Gets criterion.
int	getEstimators() Gets the number of estimators(trees) in the forest.
int	getJobs() Gets the number of jobs to run in parallel for both fit and predict.
Integer	getMaxDepth() Gets the maximum depth of the tree.
Object	getMaxFeatures() Gets number of features to consider when looking for the best split.
Integer	getMaxLeafNodes() Gets max leaf nodes.
double	getMinImpurityDecrease() Gets min impurity decrease.
Object	getMinSamplesLeaf() Gets the minimum number of samples required to be at a leaf node.
Object	getMinSamplesSplit() Gets the minimum number of samples required to split an internal node.
double	getMinWeightFractionLeaf() Gets the minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node.
Integer	getSeed() Gets the seed used by the random number generator.
boolean	isBootstrap() Whether bootstrap samples are used when building trees.
boolean	isOobScore() Whether to use out-of-bag samples to estimate the generalization accuracy.
boolean	isVerbose() Whether the verbosity of the tree building process are used.
boolean	isWarmStart() Whether warm start is used.

Methods inherited from class com.workfusion.vds.sdk.ml.python.BasePythonClassifier

close, fit, getModelName, getSerializableModel, getWorkingDir, init, predict

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Method Detail

getEstimators

public int getEstimators()

Gets the number of estimators(trees) in the forest.

Returns:

the estimators

getCriterion

public CriterionType getCriterion()

Gets criterion. The function to measure the quality of a split.

Returns:

the criterion

getMaxFeatures

public Object getMaxFeatures()

Gets number of features to consider when looking for the best split.

Returns:

the max features

getMaxDepth

public Integer getMaxDepth()

Gets the maximum depth of the tree.

Returns:

the max depth

getMinSamplesSplit

public Object getMinSamplesSplit()

Gets the minimum number of samples required to split an internal node.

Returns:

the min samples split

getMinSamplesLeaf

public Object getMinSamplesLeaf()

Gets the minimum number of samples required to be at a leaf node.

Returns:

the min samples leaf

getMinWeightFractionLeaf

public double getMinWeightFractionLeaf()

Gets the minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node.

Returns:

the min weight fraction leaf

getMaxLeafNodes

public Integer getMaxLeafNodes()

Gets max leaf nodes. Grow trees with maxLeafNodes in best-first fashion. Best nodes are defined as relative reduction in impurity.

Returns:

the max leaf nodes

getMinImpurityDecrease

public double getMinImpurityDecrease()

Gets min impurity decrease. A node will be split if this split induces a decrease of the impurity greater than or equal to this value.

Returns:

the min impurity decrease

isBootstrap

public boolean isBootstrap()

Whether bootstrap samples are used when building trees.

Returns:

the bootstrap flag

isOobScore

public boolean isOobScore()

Whether to use out-of-bag samples to estimate the generalization accuracy.

Returns:

the oob score flag

getJobs

public int getJobs()

Gets the number of jobs to run in parallel for both fit and predict. If -1, then the number of jobs is set to the number of cores.

Returns:

the jobs

getSeed

public Integer getSeed()

Gets the seed used by the random number generator.

Returns:

the seed

isVerbose

public boolean isVerbose()

Whether the verbosity of the tree building process are used.

Returns:

the verbose flag

isWarmStart

public boolean isWarmStart()

Whether warm start is used.

Returns:

the warm start flag

getClassWeight

public ClassWeightType getClassWeight()

Gets class weight.

Returns:

the class weight

builder

public static ExtraTreesClassifier.Builder builder()

Construct new builder instance.

Returns:

new builder instance