DecisionTreeModel¶

class pyspark.mllib.tree.DecisionTreeModel(java_model: py4j.java_gateway.JavaObject)[source]¶

A decision tree model for classification or regression.

New in version 1.1.0.

Methods

`call`(name, *a)	Call method of java_model
`depth`()	Get depth of tree (e.g.
`load`(sc, path)	Load a model from the given path.
`numNodes`()	Get number of nodes in tree, including leaf nodes.
`predict`(x)	Predict the label of one or more examples.
`save`(sc, path)	Save this model to the given path.
`toDebugString`()	full model.

Methods Documentation

depth() → int[source]¶: Get depth of tree (e.g. depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes).

New in version 1.1.0.

classmethod load(sc: pyspark.context.SparkContext, path: str) → JL¶: Load a model from the given path.

New in version 1.3.0.

numNodes() → int[source]¶: Get number of nodes in tree, including leaf nodes.

New in version 1.1.0.

predict(x: Union[VectorLike, pyspark.rdd.RDD[VectorLike]]) → Union[float, pyspark.rdd.RDD[float]][source]¶

Predict the label of one or more examples.

New in version 1.1.0.

Parameters

xpyspark.mllib.linalg.Vector or pyspark.RDD: Data point (feature vector), or an RDD of data points (feature vectors).

Notes

In Python, predict cannot currently be used within an RDD transformation or action. Call predict directly on the RDD instead.

save(sc: pyspark.context.SparkContext, path: str) → None¶: Save this model to the given path.

New in version 1.3.0.

KolmogorovSmirnovTestResult DecisionTree