class LassoWithSGD extends GeneralizedLinearAlgorithm[LassoModel] with Serializable
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.
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- Lasso.scala
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-    var addIntercept: BooleanWhether to add intercept (default: false). Whether to add intercept (default: false). - Attributes
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-    def createModel(weights: Vector, intercept: Double): LassoModelCreate a model given the weights and intercept Create a model given the weights and intercept - Attributes
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- LassoWithSGD → GeneralizedLinearAlgorithm
 
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-    def generateInitialWeights(input: RDD[LabeledPoint]): VectorGenerate the initial weights when the user does not supply them Generate the initial weights when the user does not supply them - Attributes
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- GeneralizedLinearAlgorithm
 
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-    def getNumFeatures: IntThe dimension of training features. The dimension of training features. - Definition Classes
- GeneralizedLinearAlgorithm
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-    def isAddIntercept: BooleanGet if the algorithm uses addIntercept Get if the algorithm uses addIntercept - Definition Classes
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-    var numFeatures: IntThe dimension of training features. The dimension of training features. - Attributes
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- GeneralizedLinearAlgorithm
 
-    var numOfLinearPredictor: IntIn 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 weightsvector which can hold both weights and intercepts. If the intercepts are added, the dimension ofweightswill be (numOfLinearPredictor) * (numFeatures + 1) . If the intercepts are not added, the dimension ofweightswill be (numOfLinearPredictor) * numFeatures.Thus, the intercepts will be encapsulated into weights, and we leave the value of intercept in GeneralizedLinearModel as zero. - Attributes
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- GeneralizedLinearAlgorithm
 
-    val optimizer: GradientDescentThe optimizer to solve the problem. The optimizer to solve the problem. - Definition Classes
- LassoWithSGD → GeneralizedLinearAlgorithm
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-    def run(input: RDD[LabeledPoint], initialWeights: Vector): LassoModelRun 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. - Definition Classes
- GeneralizedLinearAlgorithm
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- @Since("1.0.0")
 
-    def run(input: RDD[LabeledPoint]): LassoModelRun 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. - Definition Classes
- GeneralizedLinearAlgorithm
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-    def setIntercept(addIntercept: Boolean): LassoWithSGD.this.typeSet 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. - Definition Classes
- GeneralizedLinearAlgorithm
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-    def setValidateData(validateData: Boolean): LassoWithSGD.this.typeSet if the algorithm should validate data before training. Set if the algorithm should validate data before training. Default true. - Definition Classes
- GeneralizedLinearAlgorithm
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-    var validateData: Boolean- Attributes
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-    val validators: Seq[(RDD[LabeledPoint]) => Boolean]- Attributes
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- (Since version 9)