# IMPORT
>>> import numpy
>>> from numpy import allclose
>>> from pyspark.ml.linalg import Vectors
>>> from pyspark.ml.feature import StringIndexer
>>> from pyspark.ml.classification import RandomForestClassifier
# PREPARE DATA
>>> df = spark.createDataFrame([
... (1.0, Vectors.dense(1.0)),
... (0.0, Vectors.sparse(1, [], []))], ["label", "features"])
>>> stringIndexer = StringIndexer(inputCol="label", outputCol="indexed")
>>> si_model = stringIndexer.fit(df)
>>> td = si_model.transform(df)
# BUILD THE MODEL
>>> rf = RandomForestClassifier(numTrees=3, maxDepth=2, labelCol="indexed", seed=42)
>>> model = rf.fit(td)
# FEATURE IMPORTANCES
>>> model.featureImportances
SparseVector(1, {0: 1.0})
重要性：
model.featureImportances
概率：
predictions.select("probability", "label").show(1000)
probability--->即为输出概率
pandas 打乱样本：
import pandas as pd
df = pd.read_excel("window regulator01 _0914新增样本.xlsx")
df = df.sample(frac = 1) #打乱样本
pyspark train、test 随机划分
train, test = labeled_v.randomSplit([0.75, 0.25])
原文：https://www.cnblogs.com/Allen-rg/p/10445893.html