# 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

 

pyspark 模型简单实例：

 https://blog.csdn.net/Katherine_hsr/article/details/80988994

 

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])