RidgeRegressionWithSGD¶
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class
pyspark.mllib.regression.RidgeRegressionWithSGD¶ Train a regression model with L2-regularization using Stochastic Gradient Descent.
Use
pyspark.ml.regression.LinearRegressionwith elasticNetParam = 0.0. Note the default regParam is 0.01 for RidgeRegressionWithSGD, but is 0.0 for LinearRegression.Methods
train(data[, iterations, step, regParam, …])Train a regression model with L2-regularization using Stochastic Gradient Descent.
Methods Documentation
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classmethod
train(data: pyspark.rdd.RDD[pyspark.mllib.regression.LabeledPoint], iterations: int = 100, step: float = 1.0, regParam: float = 0.01, miniBatchFraction: float = 1.0, initialWeights: Optional[VectorLike] = None, intercept: bool = False, validateData: bool = True, convergenceTol: float = 0.001) → pyspark.mllib.regression.RidgeRegressionModel¶ Train a regression model with L2-regularization using Stochastic Gradient Descent. This solves the l2-regularized least squares regression formulation
f(weights) = 1/(2n) ||A weights - y||^2 + regParam/2 ||weights||^2
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.
- Parameters
- data
pyspark.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)
- initialWeights
pyspark.mllib.linalg.Vectoror 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)
- data
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classmethod