DecisionTree

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def train(input: RDD[LabeledPoint], algo: Algo, impurity: Impurity, maxDepth: Int, numClasses: Int, maxBins: Int, quantileCalculationStrategy: QuantileStrategy, categoricalFeaturesInfo: Map[Int, Int]): DecisionTreeModel

Method to train a decision tree model.
Method to train a decision tree model. The method supports binary and multiclass classification and regression.
input
Training dataset: RDD of org.apache.spark.mllib.regression.LabeledPoint. For classification, labels should take values {0, 1, ..., numClasses-1}. For regression, labels are real numbers.
algo
Type of decision tree, either classification or regression.
impurity
Criterion used for information gain calculation.
maxDepth
Maximum depth of the tree (e.g. depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes).
numClasses
Number of classes for classification. Default value of 2.
maxBins
Maximum number of bins used for splitting features.
quantileCalculationStrategy
Algorithm for calculating quantiles.
categoricalFeaturesInfo
Map storing arity of categorical features. An entry (n to k) indicates that feature n is categorical with k categories indexed from 0: {0, 1, ..., k-1}.
returns
DecisionTreeModel that can be used for prediction.

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@Since( "1.0.0" )
Note
Using org.apache.spark.mllib.tree.DecisionTree$#trainClassifier and org.apache.spark.mllib.tree.DecisionTree$#trainRegressor is recommended to clearly separate classification and regression.
def train(input: RDD[LabeledPoint], algo: Algo, impurity: Impurity, maxDepth: Int, numClasses: Int): DecisionTreeModel

Method to train a decision tree model.
Method to train a decision tree model. The method supports binary and multiclass classification and regression.
input
Training dataset: RDD of org.apache.spark.mllib.regression.LabeledPoint. For classification, labels should take values {0, 1, ..., numClasses-1}. For regression, labels are real numbers.
algo
Type of decision tree, either classification or regression.
impurity
Criterion used for information gain calculation.
maxDepth
Maximum depth of the tree (e.g. depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes).
numClasses
Number of classes for classification. Default value of 2.
returns
DecisionTreeModel that can be used for prediction.

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@Since( "1.2.0" )
Note
Using org.apache.spark.mllib.tree.DecisionTree$#trainClassifier and org.apache.spark.mllib.tree.DecisionTree$#trainRegressor is recommended to clearly separate classification and regression.
def train(input: RDD[LabeledPoint], algo: Algo, impurity: Impurity, maxDepth: Int): DecisionTreeModel

Method to train a decision tree model.
Method to train a decision tree model. The method supports binary and multiclass classification and regression.
input
Training dataset: RDD of org.apache.spark.mllib.regression.LabeledPoint. For classification, labels should take values {0, 1, ..., numClasses-1}. For regression, labels are real numbers.
algo
Type of decision tree, either classification or regression.
impurity
Criterion used for information gain calculation.
maxDepth
Maximum depth of the tree (e.g. depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes).
returns
DecisionTreeModel that can be used for prediction.

Annotations
@Since( "1.0.0" )
Note
Using org.apache.spark.mllib.tree.DecisionTree$#trainClassifier and org.apache.spark.mllib.tree.DecisionTree$#trainRegressor is recommended to clearly separate classification and regression.
def train(input: RDD[LabeledPoint], strategy: Strategy): DecisionTreeModel

Method to train a decision tree model.
Method to train a decision tree model. The method supports binary and multiclass classification and regression.
input
Training dataset: RDD of org.apache.spark.mllib.regression.LabeledPoint. For classification, labels should take values {0, 1, ..., numClasses-1}. For regression, labels are real numbers.
strategy
The configuration parameters for the tree algorithm which specify the type of decision tree (classification or regression), feature type (continuous, categorical), depth of the tree, quantile calculation strategy, etc.
returns
DecisionTreeModel that can be used for prediction.

Annotations
@Since( "1.0.0" )
Note
Using org.apache.spark.mllib.tree.DecisionTree$#trainClassifier and org.apache.spark.mllib.tree.DecisionTree$#trainRegressor is recommended to clearly separate classification and regression.
def trainClassifier(input: JavaRDD[LabeledPoint], numClasses: Int, categoricalFeaturesInfo: Map[Integer, Integer], impurity: String, maxDepth: Int, maxBins: Int): DecisionTreeModel

Java-friendly API for org.apache.spark.mllib.tree.DecisionTree$#trainClassifier
Java-friendly API for org.apache.spark.mllib.tree.DecisionTree$#trainClassifier

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@Since( "1.1.0" )
def trainClassifier(input: RDD[LabeledPoint], numClasses: Int, categoricalFeaturesInfo: Map[Int, Int], impurity: String, maxDepth: Int, maxBins: Int): DecisionTreeModel

Method to train a decision tree model for binary or multiclass classification.
Method to train a decision tree model for binary or multiclass classification.
input
Training dataset: RDD of org.apache.spark.mllib.regression.LabeledPoint. Labels should take values {0, 1, ..., numClasses-1}.
numClasses
Number of classes for classification.
categoricalFeaturesInfo
Map storing arity of categorical features. An entry (n to k) indicates that feature n is categorical with k categories indexed from 0: {0, 1, ..., k-1}.
impurity
Criterion used for information gain calculation. Supported values: "gini" (recommended) or "entropy".
maxDepth
Maximum depth of the tree (e.g. depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes). (suggested value: 5)
maxBins
Maximum number of bins used for splitting features. (suggested value: 32)
returns
DecisionTreeModel that can be used for prediction.

Annotations
@Since( "1.1.0" )
def trainRegressor(input: JavaRDD[LabeledPoint], categoricalFeaturesInfo: Map[Integer, Integer], impurity: String, maxDepth: Int, maxBins: Int): DecisionTreeModel

Java-friendly API for org.apache.spark.mllib.tree.DecisionTree$#trainRegressor
Java-friendly API for org.apache.spark.mllib.tree.DecisionTree$#trainRegressor

Annotations
@Since( "1.1.0" )
def trainRegressor(input: RDD[LabeledPoint], categoricalFeaturesInfo: Map[Int, Int], impurity: String, maxDepth: Int, maxBins: Int): DecisionTreeModel

Method to train a decision tree model for regression.
Method to train a decision tree model for regression.
input
Training dataset: RDD of org.apache.spark.mllib.regression.LabeledPoint. Labels are real numbers.
categoricalFeaturesInfo
Map storing arity of categorical features. An entry (n to k) indicates that feature n is categorical with k categories indexed from 0: {0, 1, ..., k-1}.
impurity
Criterion used for information gain calculation. The only supported value for regression is "variance".
maxDepth
Maximum depth of the tree (e.g. depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes). (suggested value: 5)
maxBins
Maximum number of bins used for splitting features. (suggested value: 32)
returns
DecisionTreeModel that can be used for prediction.

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@Since( "1.1.0" )
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Related Docs: class DecisionTree | package tree

object DecisionTree extends Serializable with Logging

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def hashCode(): Int

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

final def isInstanceOf[T0]: Boolean

def isTraceEnabled(): Boolean

def log: Logger

def logDebug(msg: ⇒ String, throwable: Throwable): Unit

def logDebug(msg: ⇒ String): Unit

def logError(msg: ⇒ String, throwable: Throwable): Unit

def logError(msg: ⇒ String): Unit

def logInfo(msg: ⇒ String, throwable: Throwable): Unit

def logInfo(msg: ⇒ String): Unit

def logName: String

def logTrace(msg: ⇒ String, throwable: Throwable): Unit

def logTrace(msg: ⇒ String): Unit

def logWarning(msg: ⇒ String, throwable: Throwable): Unit

def logWarning(msg: ⇒ String): Unit

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final def notify(): Unit

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def toString(): String

def train(input: RDD[LabeledPoint], algo: Algo, impurity: Impurity, maxDepth: Int, numClasses: Int, maxBins: Int, quantileCalculationStrategy: QuantileStrategy, categoricalFeaturesInfo: Map[Int, Int]): DecisionTreeModel

def train(input: RDD[LabeledPoint], algo: Algo, impurity: Impurity, maxDepth: Int, numClasses: Int): DecisionTreeModel

def train(input: RDD[LabeledPoint], algo: Algo, impurity: Impurity, maxDepth: Int): DecisionTreeModel

def train(input: RDD[LabeledPoint], strategy: Strategy): DecisionTreeModel

def trainClassifier(input: JavaRDD[LabeledPoint], numClasses: Int, categoricalFeaturesInfo: Map[Integer, Integer], impurity: String, maxDepth: Int, maxBins: Int): DecisionTreeModel

def trainClassifier(input: RDD[LabeledPoint], numClasses: Int, categoricalFeaturesInfo: Map[Int, Int], impurity: String, maxDepth: Int, maxBins: Int): DecisionTreeModel

def trainRegressor(input: JavaRDD[LabeledPoint], categoricalFeaturesInfo: Map[Integer, Integer], impurity: String, maxDepth: Int, maxBins: Int): DecisionTreeModel

def trainRegressor(input: RDD[LabeledPoint], categoricalFeaturesInfo: Map[Int, Int], impurity: String, maxDepth: Int, maxBins: Int): DecisionTreeModel

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

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