SVMWithSGD

class pyspark.mllib.classification.SVMWithSGD[source]

Train a Support Vector Machine (SVM) using Stochastic Gradient Descent.

New in version 0.9.0.

Methods

train(data[, iterations, step, regParam, …])

Train a support vector machine on the given data.

Methods Documentation

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

Train a support vector machine on the given data.

New in version 0.9.0.

Parameters:
datapyspark.RDD

The training data, an RDD of pyspark.mllib.regression.LabeledPoint.

iterationsint, optional

The number of iterations. (default: 100)

stepfloat, optional

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

regParamfloat, optional

The regularizer parameter. (default: 0.01)

miniBatchFractionfloat, optional

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

initialWeightspyspark.mllib.linalg.Vector or convertible, optional

The initial weights. (default: None)

regTypestr, optional

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

  • “l1” for using L1 regularization

  • “l2” for using L2 regularization (default)

  • None for no regularization

interceptbool, optional

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)

validateDatabool, optional

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

convergenceTolfloat, optional

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

SVMModel NaiveBayesModel