class RidgeRegressionWithSGD extends GeneralizedLinearAlgorithm[RidgeRegressionModel] with Serializable
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.
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- RidgeRegression.scala
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var
addIntercept: Boolean
Whether to add intercept (default: false).
Whether to add intercept (default: false).
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def
createModel(weights: Vector, intercept: Double): RidgeRegressionModel
Create a model given the weights and intercept
Create a model given the weights and intercept
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- RidgeRegressionWithSGD → GeneralizedLinearAlgorithm
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def
generateInitialWeights(input: RDD[LabeledPoint]): Vector
Generate the initial weights when the user does not supply them
Generate the initial weights when the user does not supply them
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def
getNumFeatures: Int
The dimension of training features.
The dimension of training features.
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isAddIntercept: Boolean
Get if the algorithm uses addIntercept
Get if the algorithm uses addIntercept
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var
numFeatures: Int
The dimension of training features.
The dimension of training features.
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- GeneralizedLinearAlgorithm
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var
numOfLinearPredictor: Int
In
GeneralizedLinearModel
, only single linear predictor is allowed for both weights and intercept.In
GeneralizedLinearModel
, only single linear predictor is allowed for both weights and intercept. However, for multinomial logistic regression, with K possible outcomes, we are training K-1 independent binary logistic regression models which requires K-1 sets of linear predictor.As a result, the workaround here is if more than two sets of linear predictors are needed, we construct bigger
weights
vector which can hold both weights and intercepts. If the intercepts are added, the dimension ofweights
will be (numOfLinearPredictor) * (numFeatures + 1) . If the intercepts are not added, the dimension ofweights
will be (numOfLinearPredictor) * numFeatures.Thus, the intercepts will be encapsulated into weights, and we leave the value of intercept in GeneralizedLinearModel as zero.
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val
optimizer: GradientDescent
The optimizer to solve the problem.
The optimizer to solve the problem.
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- RidgeRegressionWithSGD → GeneralizedLinearAlgorithm
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def
run(input: RDD[LabeledPoint], initialWeights: Vector): RidgeRegressionModel
Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries starting from the initial weights provided.
Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries starting from the initial weights provided.
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- @Since( "1.0.0" )
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def
run(input: RDD[LabeledPoint]): RidgeRegressionModel
Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries.
Run the algorithm with the configured parameters on an input RDD of LabeledPoint entries.
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- GeneralizedLinearAlgorithm
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def
setIntercept(addIntercept: Boolean): RidgeRegressionWithSGD.this.type
Set if the algorithm should add an intercept.
Set if the algorithm should add an intercept. Default false. We set the default to false because adding the intercept will cause memory allocation.
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- GeneralizedLinearAlgorithm
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def
setValidateData(validateData: Boolean): RidgeRegressionWithSGD.this.type
Set if the algorithm should validate data before training.
Set if the algorithm should validate data before training. Default true.
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var
validateData: Boolean
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val
validators: Seq[(RDD[LabeledPoint]) ⇒ Boolean]
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