LogisticRegressionWithSGD

class pyspark.mllib.classification.LogisticRegressionWithSGD[source]

New in version 0.9.0.

Note

Deprecated in 2.0.0. Use ml.classification.LogisticRegression or LogisticRegressionWithLBFGS.

Methods

Methods Documentation

classmethod train(data, iterations=100, step=1.0, miniBatchFraction=1.0, initialWeights=None, regParam=0.01, regType='l2', intercept=False, validateData=True, convergenceTol=0.001)[source]

Train a logistic regression model on the given data.

Parameters
  • data – The training data, an RDD of LabeledPoint.

  • iterations – The number of iterations. (default: 100)

  • step – The step parameter used in SGD. (default: 1.0)

  • miniBatchFraction – Fraction of data to be used for each SGD iteration. (default: 1.0)

  • initialWeights – The initial weights. (default: None)

  • regParam – The regularizer parameter. (default: 0.01)

  • regType

    The type of regularizer used for training our model. Supported values:

    • ”l1” for using L1 regularization

    • ”l2” for using L2 regularization (default)

    • None for no regularization

  • intercept – Boolean parameter which indicates the use or not of the augmented representation for training data (i.e., whether bias features are activated or not). (default: False)

  • validateData – Boolean parameter which indicates if the algorithm should validate data before training. (default: True)

  • convergenceTol – A condition which decides iteration termination. (default: 0.001)

New in version 0.9.0.