python spark 随机森林入门demo

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.

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