本文整理汇总了Python中pyspark.ml.classification.LogisticRegression.setParams方法的典型用法代码示例。如果您正苦于以下问题：Python LogisticRegression.setParams方法的具体用法？Python LogisticRegression.setParams怎么用？Python LogisticRegression.setParams使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pyspark.ml.classification.LogisticRegression的用法示例。
在下文中一共展示了LogisticRegression.setParams方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: oneHotEncodeColumns
# 需要导入模块: from pyspark.ml.classification import LogisticRegression [as 别名]
# 或者: from pyspark.ml.classification.LogisticRegression import setParams [as 别名]
dfhot = oneHotEncodeColumns(dfnumeric, ["workclass", "education", "marital_status", "occupation", "relationship", "race", "native_country"])
from pyspark.ml.feature import VectorAssembler
va = VectorAssembler(outputCol="features", inputCols=dfhot.columns[0:-1])
lpoints = va.transform(dfhot).select("features", "income").withColumnRenamed("income", "label")
#section 8.2.3
splits = lpoints.randomSplit([0.8, 0.2])
adulttrain = splits[0].cache()
adultvalid = splits[1].cache()
from pyspark.ml.classification import LogisticRegression
lr = LogisticRegression(regParam=0.01, maxIter=1000, fitIntercept=True)
lrmodel = lr.fit(adulttrain)
lrmodel = lr.setParams(regParam=0.01, maxIter=500, fitIntercept=True).fit(adulttrain)
lrmodel.weights
lrmodel.intercept
#section 8.2.3
validpredicts = lrmodel.transform(adultvalid)
from pyspark.ml.evaluation import BinaryClassificationEvaluator
bceval = BinaryClassificationEvaluator()
bceval.evaluate(validpredicts)
bceval.getMetricName()
bceval.setMetricName("areaUnderPR")
bceval.evaluate(validpredicts)