DecisionTree

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def findSplitsBins(input: RDD[LabeledPoint], metadata: DecisionTreeMetadata): (Array[Array[Split]], Array[Array[Bin]])

Returns splits and bins for decision tree calculation.
Returns splits and bins for decision tree calculation. Continuous and categorical features are handled differently.
Continuous features: For each feature, there are numBins - 1 possible splits representing the possible binary decisions at each node in the tree. This finds locations (feature values) for splits using a subsample of the data.
Categorical features: For each feature, there is 1 bin per split. Splits and bins are handled in 2 ways: (a) "unordered features" For multiclass classification with a low-arity feature (i.e., if isMulticlass && isSpaceSufficientForAllCategoricalSplits), the feature is split based on subsets of categories. (b) "ordered features" For regression and binary classification, and for multiclass classification with a high-arity feature, there is one bin per category.
input
Training data: RDD of org.apache.spark.mllib.regression.LabeledPoint
metadata
Learning and dataset metadata
returns
A tuple of (splits, bins). Splits is an Array of org.apache.spark.mllib.tree.model.Split of size (numFeatures, numSplits). Bins is an Array of org.apache.spark.mllib.tree.model.Bin of size (numFeatures, numBins).

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def log: Logger

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def logName: String

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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.
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.
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
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. E.g., an entry (n -> 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" )
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.
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.
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
algorithm, classification or regression
impurity
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" )
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.
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.
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
algorithm, classification or regression
impurity
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" )
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.
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.
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 algorithm (classification, regression, etc.), feature type (continuous, categorical), depth of the tree, quantile calculation strategy, etc.
returns
DecisionTreeModel that can be used for prediction

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@Since( "1.0.0" )
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. E.g., an entry (n -> 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

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@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

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@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. E.g., an entry (n -> 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: "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" )
final def wait(): Unit

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object DecisionTree extends Serializable with Logging

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final def !=(arg0: AnyRef): Boolean

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: AnyRef): Boolean

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

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final def eq(arg0: AnyRef): Boolean

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def finalize(): Unit

def findSplitsBins(input: RDD[LabeledPoint], metadata: DecisionTreeMetadata): (Array[Array[Split]], Array[Array[Bin]])

final def getClass(): Class[_]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

def isTraceEnabled(): Boolean

def log: Logger

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

def logDebug(msg: ⇒ String): Unit

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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

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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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