DecisionTree

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def findBestSplits(input: RDD[TreePoint], parentImpurities: Array[Double], metadata: DecisionTreeMetadata, level: Int, nodes: Array[Node], splits: Array[Array[Split]], bins: Array[Array[Bin]], maxLevelForSingleGroup: Int, timer: TimeTracker = new TimeTracker): Array[(Split, InformationGainStats)]

Returns an array of optimal splits for all nodes at a given level.
Returns an array of optimal splits for all nodes at a given level. Splits the task into multiple groups if the level-wise training task could lead to memory overflow.
input
Training data: RDD of org.apache.spark.mllib.tree.impl.TreePoint
parentImpurities
Impurities for all parent nodes for the current level
metadata
Learning and dataset metadata
level
Level of the tree
splits
possible splits for all features
bins
possible bins for all features
maxLevelForSingleGroup
the deepest level for single-group level-wise computation.
returns
array (over nodes) of splits with best split for each node at a given level.

Attributes
protected[org.apache.spark.mllib.tree]
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.
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. There are (1 << maxFeatureValue - 1) - 1 splits. (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, numBins - 1). Bins is an Array of org.apache.spark.mllib.tree.model.Bin of size (numFeatures, numBins).

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protected[org.apache.spark.mllib.tree]
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def train(input: RDD[LabeledPoint], algo: Algo, impurity: Impurity, maxDepth: Int, numClassesForClassification: 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.
numClassesForClassification
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
def train(input: RDD[LabeledPoint], algo: Algo, impurity: Impurity, maxDepth: Int, numClassesForClassification: 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.
numClassesForClassification
number of classes for classification. Default value of 2.
returns
DecisionTreeModel that can be used for prediction
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
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
def trainClassifier(input: JavaRDD[LabeledPoint], numClassesForClassification: Int, categoricalFeaturesInfo: Map[Integer, Integer], impurity: String, maxDepth: Int, maxBins: Int): DecisionTreeModel

Java-friendly API for org.apache.spark.mllib.tree.DecisionTree$#trainClassifier
def trainClassifier(input: RDD[LabeledPoint], numClassesForClassification: 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}.
numClassesForClassification
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: 4)
maxBins
maximum number of bins used for splitting features (suggested value: 100)
returns
DecisionTreeModel that can be used for prediction
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
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: 4)
maxBins
maximum number of bins used for splitting features (suggested value: 100)
returns
DecisionTreeModel that can be used for prediction
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object DecisionTree extends Serializable with Logging

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def findBestSplits(input: RDD[TreePoint], parentImpurities: Array[Double], metadata: DecisionTreeMetadata, level: Int, nodes: Array[Node], splits: Array[Array[Split]], bins: Array[Array[Bin]], maxLevelForSingleGroup: Int, timer: TimeTracker = new TimeTracker): Array[(Split, InformationGainStats)]

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

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def log: Logger

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

def train(input: RDD[LabeledPoint], algo: Algo, impurity: Impurity, maxDepth: Int, numClassesForClassification: 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], numClassesForClassification: Int, categoricalFeaturesInfo: Map[Integer, Integer], impurity: String, maxDepth: Int, maxBins: Int): DecisionTreeModel

def trainClassifier(input: RDD[LabeledPoint], numClassesForClassification: 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

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final def wait(arg0: Long): Unit

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