Sklearn Ridge Regression

Ridge Regression, solves a regression model where the loss function is the linear least squares function and regularization is given by the l2-norm.

Type

ml-estimator

Class

fire.nodes.sklearn.NodeSklearnRidgeRegression

Fields

Name

Title

Description

targetCol

Target Column

The label column for model fitting

featureCols

Feature Columns

Feature columns of type - all numeric, boolean and vector

splitRatio

Split Ratio

Split Ratio

alpha

Alpha

Constant that multiplies the L2 term, regularization strength.

fitintercept

Fit Intercept

Whether to calculate the intercept for this model.

normalize

Normalize

This parameter is ignored when fit_intercept is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm.

maxiter

Max Iterations

Maximum number of iterations for conjugate gradient solver.

tol

Tolerance

Precision of the solution.

solver

Solver

Solver to use in the computational routines.

randomstate

Random State

The seed of the pseudo random number generator to use when shuffling the data for the solver.’