class pyspark.mllib.tree.RandomForest[source]
Learning algorithm for a random forest model for classification or regression.
New in version 1.2.0.
- supportedFeatureSubsetStrategies = ('auto', 'all', 'sqrt', 'log2', 'onethird')
- classmethod trainClassifier(data, numClasses, categoricalFeaturesInfo, numTrees, featureSubsetStrategy='auto', impurity='gini', maxDepth=4, maxBins=32, seed=None)[source]
-
Train a random forest model for binary or multiclass classification.
Parameters: - data – Training dataset: RDD of 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 -> k) indicates that feature n is categorical with k categories indexed from 0: {0, 1, ..., k-1}.
- numTrees – Number of trees in the random forest.
- featureSubsetStrategy – Number of features to consider for splits at each node. Supported values: “auto”, “all”, “sqrt”, “log2”, “onethird”. If “auto” is set, this parameter is set based on numTrees: if numTrees == 1, set to “all”; if numTrees > 1 (forest) set to “sqrt”. (default: “auto”)
- impurity – Criterion used for information gain calculation. Supported values: “gini” or “entropy”. (default: “gini”)
- maxDepth – Maximum depth of tree (e.g. depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes). (default: 4)
- maxBins – Maximum number of bins used for splitting features. (default: 32)
- seed – Random seed for bootstrapping and choosing feature subsets. Set as None to generate seed based on system time. (default: None)
Returns: RandomForestModel that can be used for prediction.
Example usage:
>>> from pyspark.mllib.regression import LabeledPoint
>>> from pyspark.mllib.tree import RandomForest
>>>
>>> data = [
... LabeledPoint(0.0, [0.0]),
... LabeledPoint(0.0, [1.0]),
... LabeledPoint(1.0, [2.0]),
... LabeledPoint(1.0, [3.0])
... ]
>>> model = RandomForest.trainClassifier(sc.parallelize(data), 2, {}, 3, seed=42)
>>> model.numTrees()
3
>>> model.totalNumNodes()
7
>>> print(model)
TreeEnsembleModel classifier with 3 trees >>> print(model.toDebugString())
TreeEnsembleModel classifier with 3 trees Tree 0:
Predict: 1.0
Tree 1:
If (feature 0 <= 1.0)
Predict: 0.0
Else (feature 0 > 1.0)
Predict: 1.0
Tree 2:
If (feature 0 <= 1.0)
Predict: 0.0
Else (feature 0 > 1.0)
Predict: 1.0 >>> model.predict([2.0])
1.0
>>> model.predict([0.0])
0.0
>>> rdd = sc.parallelize([[3.0], [1.0]])
>>> model.predict(rdd).collect()
[1.0, 0.0]New in version 1.2.0.
摘自：https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html#pyspark.mllib.tree.DecisionTree