com.workfusion.vds.sdk.ml.sklearn.randomforest

Class RandomForestClassifier.Builder<B extends RandomForestClassifier.Builder,T extends RandomForestClassifier>

java.lang.Object

com.workfusion.vds.sdk.ml.python.BasePythonClassifier.Builder<RandomForestClassifier.Builder<B,T>,T>

com.workfusion.vds.sdk.ml.sklearn.randomforest.RandomForestClassifier.Builder<B,T>

Direct Known Subclasses:

RandomForestParamGridClassifier.Builder

Enclosing class:

RandomForestClassifier

public static class RandomForestClassifier.Builder<B extends RandomForestClassifier.Builder,T extends RandomForestClassifier>
extends BasePythonClassifier.Builder<RandomForestClassifier.Builder<B,T>,T>

The type Builder.

Field Summary

Fields inherited from class com.workfusion.vds.sdk.ml.python.BasePythonClassifier.Builder

modelName, workingDir

Constructor Summary

Constructors

Constructor and Description
Builder() Instantiates a new Builder.

Method Summary

Modifier and Type	Method and Description
B	bootstrap(boolean bootstrap) Sets whether bootstrap samples are used when building trees.
T	build() Build classifier.
B	classWeight(ClassWeightType classWeight) Sets class weight.
B	criterion(CriterionType criterion) Sets the function to measure the quality of a split.
B	estimators(int estimators) Sets the number of estimators(trees) in the forest.
B	jobs(int jobs) Sets the number of jobs to run in parallel for both fit and predict.
B	maxDepth(Integer maxDepth) Sets the maximum depth of the tree.
B	maxFeatures(double maxFeatures) Sets the fraction of features to consider when looking for the best split.
B	maxFeatures(int maxFeatures) Sets the maximum number of features to consider when looking for the best split.
B	maxFeatures(MaxFeaturesType maxFeatures) Sets the maximum number of features to consider when looking for the best split.
B	maxLeafNodes(Integer maxLeafNodes) Sets max leaf nodes.
B	minImpurityDecrease(double minImpurityDecrease) Sets min impurity decrease.
B	minSamplesLeaf(double minSamplesLeaf) Sets the minimum number of samples required to be at a leaf node (the fraction of the samples).
B	minSamplesLeaf(int minSamplesLeaf) Sets the minimum number of samples required to be at a leaf node.
B	minSamplesSplit(double minSamplesSplit) Sets the minimum number of samples required to split an internal node (the fraction of the samples).
B	minSamplesSplit(int minSamplesSplit) Sets the minimum number of samples required to split an internal node.
B	minWeightFractionLeaf(double minWeightFractionLeaf) Sets the minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node.
B	modelName(String name) Set a model name.
B	oobScore(boolean oobScore) Sets whether to use out-of-bag samples to estimate the generalization accuracy.
B	seed(Integer seed) Sets the seed used by the random number generator.
B	verbose(boolean verbose) Sets whether warm start is used.
B	warmStart(boolean warmStart) Sets whether warm start is used.

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

createDefaultClassifierBridge, workingDir

Methods inherited from class java.lang.Object

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

Constructor Detail

Builder

public Builder()

Instantiates a new Builder.

Method Detail

estimators

public B estimators(int estimators)

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

Parameters:

estimators - the number of estimators (default: 10)

Returns:

the builder

criterion

public B criterion(CriterionType criterion)

Sets the function to measure the quality of a split.

Parameters:

criterion - the criterion (CriterionType) (default: CriterionType.GINI)

Returns:

the builder

maxFeatures

public B maxFeatures(MaxFeaturesType maxFeatures)

Sets the maximum number of features to consider when looking for the best split. If null then maxFeatures=n_features.

Parameters:

maxFeatures - the max features (MaxFeaturesType) (default: MaxFeaturesType.AUTO)

Returns:

the builder

maxFeatures

public B maxFeatures(int maxFeatures)

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

Parameters:

maxFeatures - the max features

Returns:

the builder

maxFeatures

public B maxFeatures(double maxFeatures)

Sets the fraction of features to consider when looking for the best split.

Parameters:

maxFeatures - the max features

Returns:

the builder

maxDepth

public B maxDepth(Integer maxDepth)

Sets the maximum depth of the tree. If null then nodes are expanded until all leaves are pure or until all leaves contain less than minSamplesSplit samples.

Parameters:

maxDepth - the max depth (default: null)

Returns:

the builder

minSamplesSplit

public B minSamplesSplit(double minSamplesSplit)

Sets the minimum number of samples required to split an internal node (the fraction of the samples).

Parameters:

minSamplesSplit - the min samples split

Returns:

the builder

minSamplesSplit

public B minSamplesSplit(int minSamplesSplit)

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

Parameters:

minSamplesSplit - the min samples split (default: 2)

Returns:

the builder

minSamplesLeaf

public B minSamplesLeaf(int minSamplesLeaf)

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

Parameters:

minSamplesLeaf - the min samples leaf (default: 1)

Returns:

the builder

minSamplesLeaf

public B minSamplesLeaf(double minSamplesLeaf)

Sets the minimum number of samples required to be at a leaf node (the fraction of the samples).

Parameters:

minSamplesLeaf - the min samples leaf

Returns:

the builder

minWeightFractionLeaf

public B minWeightFractionLeaf(double minWeightFractionLeaf)

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

Parameters:

minWeightFractionLeaf - the min weight fraction leaf (default: 0.0)

Returns:

the builder

maxLeafNodes

public B maxLeafNodes(Integer maxLeafNodes)

Sets max leaf nodes. Grow trees with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction in impurity. If null then unlimited number of leaf nodes.

Parameters:

maxLeafNodes - the max leaf nodes (default: null)

Returns:

the builder

minImpurityDecrease

public B minImpurityDecrease(double minImpurityDecrease)

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

Parameters:

minImpurityDecrease - the min impurity decrease (default: 0.0)

Returns:

the builder

bootstrap

public B bootstrap(boolean bootstrap)

Sets whether bootstrap samples are used when building trees.

Parameters:

bootstrap - the bootstrap (default: true)

Returns:

the builder

oobScore

public B oobScore(boolean oobScore)

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

Parameters:

oobScore - the oob score (default: false)

Returns:

the builder

jobs

public B jobs(int jobs)

Sets 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.

Parameters:

jobs - the number of jobs (default: 1)

Returns:

the builder

seed

public B seed(Integer seed)

Sets the seed used by the random number generator. If null then default random number generator is used (np.random).

Parameters:

seed - the seed (default: null)

Returns:

the builder

verbose

public B verbose(boolean verbose)

Sets whether warm start is used.

Parameters:

verbose - the verbose mode (default: false)

Returns:

the builder

warmStart

public B warmStart(boolean warmStart)

Sets whether warm start is used.

Parameters:

warmStart - the warm start (default: false)

Returns:

the builder

classWeight

public B classWeight(ClassWeightType classWeight)

Sets class weight. If null then all classes are supposed to have weight one.

Parameters:

classWeight - the class weight (ClassWeightType) (default: null)

Returns:

the builder

modelName

public B modelName(String name)

Description copied from class: BasePythonClassifier.Builder

Set a model name.

Overrides:

modelName in class BasePythonClassifier.Builder<RandomForestClassifier.Builder<B extends RandomForestClassifier.Builder,T extends RandomForestClassifier>,T extends RandomForestClassifier>

Parameters:

name - the model name (String)

Returns:

the builder

build

public T build()

Description copied from class: BasePythonClassifier.Builder

Build classifier.

Specified by:

build in class BasePythonClassifier.Builder<RandomForestClassifier.Builder<B extends RandomForestClassifier.Builder,T extends RandomForestClassifier>,T extends RandomForestClassifier>

Returns:

the classifier instance