# LassoWithSGD¶

class pyspark.mllib.regression.LassoWithSGD[source]

Train a regression model with L1-regularization using Stochastic Gradient Descent.

New in version 0.9.0.

Deprecated since version 2.0.0: Use pyspark.ml.regression.LinearRegression with elasticNetParam = 1.0. Note the default regParam is 0.01 for LassoWithSGD, but is 0.0 for LinearRegression.

Methods

 train(data[, iterations, step, regParam, …]) Train a regression model with L1-regularization using Stochastic Gradient Descent.

Methods Documentation

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

Train a regression model with L1-regularization using Stochastic Gradient Descent. This solves the l1-regularized least squares regression formulation

f(weights) = 1/(2n) ||A weights - y||^2 + regParam ||weights||_1

Here the data matrix has n rows, and the input RDD holds the set of rows of A, each with its corresponding right hand side label y. See also the documentation for the precise formulation.

New in version 0.9.0.

Parameters
datapyspark.RDD

The training data, an RDD of 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)

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)