DecisionTree#
- class pyspark.mllib.tree.DecisionTree[source]#
Learning algorithm for a decision tree model for classification or regression.
New in version 1.1.0.
Methods
trainClassifier(data, numClasses, ...[, ...])Train a decision tree model for classification.
trainRegressor(data, categoricalFeaturesInfo)Train a decision tree model for regression.
Methods Documentation
- classmethod trainClassifier(data, numClasses, categoricalFeaturesInfo, impurity='gini', maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0)[source]#
Train a decision tree model for classification.
New in version 1.1.0.
- Parameters
- data
pyspark.RDD Training data: RDD of LabeledPoint. Labels should take values {0, 1, …, numClasses-1}.
- numClassesint
Number of classes for classification.
- categoricalFeaturesInfodict
Map storing arity of categorical features. An entry (n -> k) indicates that feature n is categorical with k categories indexed from 0: {0, 1, …, k-1}.
- impuritystr, optional
Criterion used for information gain calculation. Supported values: “gini” or “entropy”. (default: “gini”)
- maxDepthint, optional
Maximum depth of tree (e.g. depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes). (default: 5)
- maxBinsint, optional
Number of bins used for finding splits at each node. (default: 32)
- minInstancesPerNodeint, optional
Minimum number of instances required at child nodes to create the parent split. (default: 1)
- minInfoGainfloat, optional
Minimum info gain required to create a split. (default: 0.0)
- data
- Returns
Examples
>>> from numpy import array >>> from pyspark.mllib.regression import LabeledPoint >>> from pyspark.mllib.tree import DecisionTree >>> >>> data = [ ... LabeledPoint(0.0, [0.0]), ... LabeledPoint(1.0, [1.0]), ... LabeledPoint(1.0, [2.0]), ... LabeledPoint(1.0, [3.0]) ... ] >>> model = DecisionTree.trainClassifier(sc.parallelize(data), 2, {}) >>> print(model) DecisionTreeModel classifier of depth 1 with 3 nodes
>>> print(model.toDebugString()) DecisionTreeModel classifier of depth 1 with 3 nodes If (feature 0 <= 0.5) Predict: 0.0 Else (feature 0 > 0.5) Predict: 1.0 >>> model.predict(array([1.0])) 1.0 >>> model.predict(array([0.0])) 0.0 >>> rdd = sc.parallelize([[1.0], [0.0]]) >>> model.predict(rdd).collect() [1.0, 0.0]
- classmethod trainRegressor(data, categoricalFeaturesInfo, impurity='variance', maxDepth=5, maxBins=32, minInstancesPerNode=1, minInfoGain=0.0)[source]#
Train a decision tree model for regression.
- Parameters
- data
pyspark.RDD Training data: RDD of LabeledPoint. Labels are real numbers.
- categoricalFeaturesInfodict
Map storing arity of categorical features. An entry (n -> k) indicates that feature n is categorical with k categories indexed from 0: {0, 1, …, k-1}.
- impuritystr, optional
Criterion used for information gain calculation. The only supported value for regression is “variance”. (default: “variance”)
- maxDepthint, optional
Maximum depth of tree (e.g. depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes). (default: 5)
- maxBinsint, optional
Number of bins used for finding splits at each node. (default: 32)
- minInstancesPerNodeint, optional
Minimum number of instances required at child nodes to create the parent split. (default: 1)
- minInfoGainfloat, optional
Minimum info gain required to create a split. (default: 0.0)
- data
- Returns
Examples
>>> from pyspark.mllib.regression import LabeledPoint >>> from pyspark.mllib.tree import DecisionTree >>> from pyspark.mllib.linalg import SparseVector >>> >>> sparse_data = [ ... LabeledPoint(0.0, SparseVector(2, {0: 0.0})), ... LabeledPoint(1.0, SparseVector(2, {1: 1.0})), ... LabeledPoint(0.0, SparseVector(2, {0: 0.0})), ... LabeledPoint(1.0, SparseVector(2, {1: 2.0})) ... ] >>> >>> model = DecisionTree.trainRegressor(sc.parallelize(sparse_data), {}) >>> model.predict(SparseVector(2, {1: 1.0})) 1.0 >>> model.predict(SparseVector(2, {1: 0.0})) 0.0 >>> rdd = sc.parallelize([[0.0, 1.0], [0.0, 0.0]]) >>> model.predict(rdd).collect() [1.0, 0.0]
New in version 1.1.0.