本文整理汇总了Python中pyspark.ml.classification.LogisticRegression.setRegParam方法的典型用法代码示例。如果您正苦于以下问题：Python LogisticRegression.setRegParam方法的具体用法？Python LogisticRegression.setRegParam怎么用？Python LogisticRegression.setRegParam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.setRegParam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: Row
# 需要导入模块: from pyspark.ml.classification import LogisticRegression [as 别名]
# 或者: from pyspark.ml.classification.LogisticRegression import setRegParam [as 别名]
# A LabeledPoint is an Object with two fields named label and features
# and Spark SQL identifies these fields and creates the schema appropriately.
training = spark.createDataFrame([
Row(label=1.0, features=DenseVector([0.0, 1.1, 0.1])),
Row(label=0.0, features=DenseVector([2.0, 1.0, -1.0])),
Row(label=0.0, features=DenseVector([2.0, 1.3, 1.0])),
Row(label=1.0, features=DenseVector([0.0, 1.2, -0.5]))])
# Create a LogisticRegression instance with maxIter = 10.
# This instance is an Estimator.
lr = LogisticRegression(maxIter=10)
# Print out the parameters, documentation, and any default values.
print("LogisticRegression parameters:\n" + lr.explainParams() + "\n")
# We may also set parameters using setter methods.
lr.setRegParam(0.01)
# Learn a LogisticRegression model. This uses the parameters stored in lr.
model1 = lr.fit(training)
# Since model1 is a Model (i.e., a Transformer produced by an Estimator),
# we can view the parameters it used during fit().
# This prints the parameter (name: value) pairs, where names are unique IDs for this
# LogisticRegression instance.
print("Model 1 was fit using parameters:\n")
pprint.pprint(model1.extractParamMap())
# We may alternatively specify parameters using a parameter map.
# paramMap overrides all lr parameters set earlier.
paramMap = {lr.maxIter: 20, lr.thresholds: [0.5, 0.5], lr.probabilityCol: "myProbability"}