Strategy (Spark 3.5.3 JavaDoc)

Object
- org.apache.spark.mllib.tree.configuration.Strategy

All Implemented Interfaces:

java.io.Serializable
```
public class Strategy
extends Object
implements scala.Serializable
```
Stores all the configuration options for tree construction param: algo Learning goal. Supported: org.apache.spark.mllib.tree.configuration.Algo.Classification, org.apache.spark.mllib.tree.configuration.Algo.Regression param: impurity Criterion used for information gain calculation. Supported for Classification: Gini, Entropy. Supported for Regression: Variance. param: maxDepth Maximum depth of the tree (e.g. depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes). param: numClasses Number of classes for classification. (Ignored for regression.) Default value is 2 (binary classification). param: 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. param: quantileCalculationStrategy Algorithm for calculating quantiles. Supported: org.apache.spark.mllib.tree.configuration.QuantileStrategy.Sort param: categoricalFeaturesInfo A map storing information about the categorical variables and the number of discrete values they take. An entry (n to k) indicates that feature n is categorical with k categories indexed from 0: {0, 1, ..., k-1}. param: minInstancesPerNode Minimum number of instances each child must have after split. Default value is 1. If a split cause left or right child to have less than minInstancesPerNode, this split will not be considered as a valid split. param: minInfoGain Minimum information gain a split must get. Default value is 0.0. If a split has less information gain than minInfoGain, this split will not be considered as a valid split. param: maxMemoryInMB Maximum memory in MB allocated to histogram aggregation. Default value is 256 MB. If too small, then 1 node will be split per iteration, and its aggregates may exceed this size. param: subsamplingRate Fraction of the training data used for learning decision tree. param: useNodeIdCache If this is true, instead of passing trees to executors, the algorithm will maintain a separate RDD of node Id cache for each row. param: checkpointInterval How often to checkpoint when the node Id cache gets updated. E.g. 10 means that the cache will get checkpointed every 10 updates. If the checkpoint directory is not set in SparkContext, this setting is ignored.

See Also:

Serialized Form

Constructor Summary

Constructors
Constructor and Description
`Strategy(scala.Enumeration.Value algo, Impurity impurity, int maxDepth, int numClasses, int maxBins, scala.Enumeration.Value quantileCalculationStrategy, scala.collection.immutable.Map<Object,Object> categoricalFeaturesInfo, int minInstancesPerNode, double minInfoGain, int maxMemoryInMB, double subsamplingRate, boolean useNodeIdCache, int checkpointInterval)` Backwards compatible constructor for `Strategy`
`Strategy(scala.Enumeration.Value algo, Impurity impurity, int maxDepth, int numClasses, int maxBins, scala.Enumeration.Value quantileCalculationStrategy, scala.collection.immutable.Map<Object,Object> categoricalFeaturesInfo, int minInstancesPerNode, double minInfoGain, int maxMemoryInMB, double subsamplingRate, boolean useNodeIdCache, int checkpointInterval, double minWeightFractionPerNode, boolean bootstrap)`
`Strategy(scala.Enumeration.Value algo, Impurity impurity, int maxDepth, int numClasses, int maxBins, java.util.Map<Integer,Integer> categoricalFeaturesInfo)` Java-friendly constructor for `Strategy`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`scala.Enumeration.Value`	`algo()`
`scala.collection.immutable.Map<Object,Object>`	`categoricalFeaturesInfo()`
`int`	`checkpointInterval()`
`Strategy`	`copy()` Returns a shallow copy of this instance.
`static Strategy`	`defaultStrategy(scala.Enumeration.Value algo)` Construct a default set of parameters for `DecisionTree`
`static Strategy`	`defaultStrategy(String algo)` Construct a default set of parameters for `DecisionTree`
`scala.Enumeration.Value`	`getAlgo()`
`scala.collection.immutable.Map<Object,Object>`	`getCategoricalFeaturesInfo()`
`int`	`getCheckpointInterval()`
`Impurity`	`getImpurity()`
`int`	`getMaxBins()`
`int`	`getMaxDepth()`
`int`	`getMaxMemoryInMB()`
`double`	`getMinInfoGain()`
`int`	`getMinInstancesPerNode()`
`double`	`getMinWeightFractionPerNode()`
`int`	`getNumClasses()`
`scala.Enumeration.Value`	`getQuantileCalculationStrategy()`
`double`	`getSubsamplingRate()`
`boolean`	`getUseNodeIdCache()`
`Impurity`	`impurity()`
`boolean`	`isMulticlassClassification()`
`boolean`	`isMulticlassWithCategoricalFeatures()`
`int`	`maxBins()`
`int`	`maxDepth()`
`int`	`maxMemoryInMB()`
`double`	`minInfoGain()`
`int`	`minInstancesPerNode()`
`double`	`minWeightFractionPerNode()`
`int`	`numClasses()`
`scala.Enumeration.Value`	`quantileCalculationStrategy()`
`void`	`setAlgo(scala.Enumeration.Value x$1)`
`void`	`setAlgo(String algo)` Sets Algorithm using a String.
`void`	`setCategoricalFeaturesInfo(java.util.Map<Integer,Integer> categoricalFeaturesInfo)` Sets categoricalFeaturesInfo using a Java Map.
`void`	`setCategoricalFeaturesInfo(scala.collection.immutable.Map<Object,Object> x$1)`
`void`	`setCheckpointInterval(int x$1)`
`void`	`setImpurity(Impurity x$1)`
`void`	`setMaxBins(int x$1)`
`void`	`setMaxDepth(int x$1)`
`void`	`setMaxMemoryInMB(int x$1)`
`void`	`setMinInfoGain(double x$1)`
`void`	`setMinInstancesPerNode(int x$1)`
`void`	`setMinWeightFractionPerNode(double x$1)`
`void`	`setNumClasses(int x$1)`
`void`	`setQuantileCalculationStrategy(scala.Enumeration.Value x$1)`
`void`	`setSubsamplingRate(double x$1)`
`void`	`setUseNodeIdCache(boolean x$1)`
`double`	`subsamplingRate()`
`boolean`	`useNodeIdCache()`

Methods inherited from class Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

Strategy

public Strategy(scala.Enumeration.Value algo,
                Impurity impurity,
                int maxDepth,
                int numClasses,
                int maxBins,
                scala.Enumeration.Value quantileCalculationStrategy,
                scala.collection.immutable.Map<Object,Object> categoricalFeaturesInfo,
                int minInstancesPerNode,
                double minInfoGain,
                int maxMemoryInMB,
                double subsamplingRate,
                boolean useNodeIdCache,
                int checkpointInterval,
                double minWeightFractionPerNode,
                boolean bootstrap)

Strategy

public Strategy(scala.Enumeration.Value algo,
                Impurity impurity,
                int maxDepth,
                int numClasses,
                int maxBins,
                scala.Enumeration.Value quantileCalculationStrategy,
                scala.collection.immutable.Map<Object,Object> categoricalFeaturesInfo,
                int minInstancesPerNode,
                double minInfoGain,
                int maxMemoryInMB,
                double subsamplingRate,
                boolean useNodeIdCache,
                int checkpointInterval)

Backwards compatible constructor for Strategy

Parameters:: algo - (undocumented); impurity - (undocumented); maxDepth - (undocumented); numClasses - (undocumented); maxBins - (undocumented); quantileCalculationStrategy - (undocumented); categoricalFeaturesInfo - (undocumented); minInstancesPerNode - (undocumented); minInfoGain - (undocumented); maxMemoryInMB - (undocumented); subsamplingRate - (undocumented); useNodeIdCache - (undocumented); checkpointInterval - (undocumented)

Strategy

public Strategy(scala.Enumeration.Value algo,
                Impurity impurity,
                int maxDepth,
                int numClasses,
                int maxBins,
                java.util.Map<Integer,Integer> categoricalFeaturesInfo)

Java-friendly constructor for Strategy

Parameters:: algo - (undocumented); impurity - (undocumented); maxDepth - (undocumented); numClasses - (undocumented); maxBins - (undocumented); categoricalFeaturesInfo - (undocumented)

Method Detail

defaultStrategy
```
public static Strategy defaultStrategy(String algo)
```
Construct a default set of parameters for DecisionTree

Parameters:

algo - "Classification" or "Regression"

Returns:

(undocumented)

defaultStrategy
```
public static Strategy defaultStrategy(scala.Enumeration.Value algo)
```
Construct a default set of parameters for DecisionTree

Parameters:

algo - Algo.Classification or Algo.Regression

Returns:

(undocumented)

algo
```
public scala.Enumeration.Value algo()
```

impurity
```
public Impurity impurity()
```

maxDepth
```
public int maxDepth()
```

numClasses
```
public int numClasses()
```

maxBins
```
public int maxBins()
```

quantileCalculationStrategy

public scala.Enumeration.Value quantileCalculationStrategy()

categoricalFeaturesInfo

public scala.collection.immutable.Map<Object,Object> categoricalFeaturesInfo()

minInstancesPerNode
```
public int minInstancesPerNode()
```

minInfoGain
```
public double minInfoGain()
```

maxMemoryInMB
```
public int maxMemoryInMB()
```

subsamplingRate
```
public double subsamplingRate()
```

useNodeIdCache
```
public boolean useNodeIdCache()
```

checkpointInterval
```
public int checkpointInterval()
```

minWeightFractionPerNode

public double minWeightFractionPerNode()

isMulticlassClassification
```
public boolean isMulticlassClassification()
```
Returns:

(undocumented)

isMulticlassWithCategoricalFeatures
```
public boolean isMulticlassWithCategoricalFeatures()
```
Returns:

(undocumented)

setAlgo
```
public void setAlgo(String algo)
```
Sets Algorithm using a String.

Parameters:

algo - (undocumented)

setCategoricalFeaturesInfo
```
public void setCategoricalFeaturesInfo(java.util.Map<Integer,Integer> categoricalFeaturesInfo)
```
Sets categoricalFeaturesInfo using a Java Map.

Parameters:

categoricalFeaturesInfo - (undocumented)

copy
```
public Strategy copy()
```
Returns a shallow copy of this instance.

Returns:

(undocumented)

getAlgo

public scala.Enumeration.Value getAlgo()

getCategoricalFeaturesInfo

public scala.collection.immutable.Map<Object,Object> getCategoricalFeaturesInfo()

getCheckpointInterval
```
public int getCheckpointInterval()
```

getImpurity
```
public Impurity getImpurity()
```

getMaxBins
```
public int getMaxBins()
```

getMaxDepth
```
public int getMaxDepth()
```

getMaxMemoryInMB
```
public int getMaxMemoryInMB()
```

getMinInfoGain
```
public double getMinInfoGain()
```

getMinInstancesPerNode
```
public int getMinInstancesPerNode()
```

getMinWeightFractionPerNode

public double getMinWeightFractionPerNode()

getNumClasses
```
public int getNumClasses()
```

getQuantileCalculationStrategy

public scala.Enumeration.Value getQuantileCalculationStrategy()

getSubsamplingRate
```
public double getSubsamplingRate()
```

getUseNodeIdCache
```
public boolean getUseNodeIdCache()
```

setAlgo

public void setAlgo(scala.Enumeration.Value x$1)

setCategoricalFeaturesInfo

public void setCategoricalFeaturesInfo(scala.collection.immutable.Map<Object,Object> x$1)

setCheckpointInterval

public void setCheckpointInterval(int x$1)

setImpurity

public void setImpurity(Impurity x$1)

setMaxBins
```
public void setMaxBins(int x$1)
```

setMaxDepth
```
public void setMaxDepth(int x$1)
```

setMaxMemoryInMB

public void setMaxMemoryInMB(int x$1)

setMinInfoGain

public void setMinInfoGain(double x$1)

setMinInstancesPerNode

public void setMinInstancesPerNode(int x$1)

setMinWeightFractionPerNode

public void setMinWeightFractionPerNode(double x$1)

setNumClasses
```
public void setNumClasses(int x$1)
```

setQuantileCalculationStrategy

public void setQuantileCalculationStrategy(scala.Enumeration.Value x$1)

setSubsamplingRate

public void setSubsamplingRate(double x$1)

setUseNodeIdCache

public void setUseNodeIdCache(boolean x$1)

Class Strategy

Constructor Summary

Method Summary

Methods inherited from class Object

Constructor Detail

Strategy

Strategy

Strategy

Method Detail

defaultStrategy

defaultStrategy

algo

impurity

maxDepth

numClasses

maxBins

quantileCalculationStrategy

categoricalFeaturesInfo

minInstancesPerNode

minInfoGain

maxMemoryInMB

subsamplingRate

useNodeIdCache

checkpointInterval

minWeightFractionPerNode

isMulticlassClassification

isMulticlassWithCategoricalFeatures

setAlgo

setCategoricalFeaturesInfo

copy

getAlgo

getCategoricalFeaturesInfo

getCheckpointInterval

getImpurity

getMaxBins

getMaxDepth

getMaxMemoryInMB

getMinInfoGain

getMinInstancesPerNode

getMinWeightFractionPerNode

getNumClasses

getQuantileCalculationStrategy

getSubsamplingRate

getUseNodeIdCache

setAlgo

setCategoricalFeaturesInfo

setCheckpointInterval

setImpurity

setMaxBins

setMaxDepth

setMaxMemoryInMB

setMinInfoGain

setMinInstancesPerNode

setMinWeightFractionPerNode

setNumClasses

setQuantileCalculationStrategy

setSubsamplingRate

setUseNodeIdCache