Sklearn Bayesian Ridge Regression

Bayesian regression allows a natural mechanism to survive insufficient data or poorly distributed data by formulating linear regression using probability distributors rather than point estimates. The output or response ‘y’ is assumed to drawn from a probability distribution rather than estimated as a single value.

Type

ml-estimator

Class

fire.nodes.sklearn.NodeSklearnBayesianRidgeRegression

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

niter

Number of Iterations

Maximum number of iterations. Should be greater than or equal to 1.

alpha1

Alpha1

Hyper-parameter : shape parameter for the Gamma distribution prior over the alpha parameter.

alpha2

Alpha2

Hyper-parameter : inverse scale parameter (rate parameter) for the Gamma distribution prior over the alpha parameter.

lambda1

Lambda1

Hyper-parameter : shape parameter for the Gamma distribution prior over the lambda parameter.

lambda2

Lambda2

Hyper-parameter : inverse scale parameter (rate parameter) for the Gamma distribution prior over the lambda parameter.

tol

Tolerance

Stop the algorithm if w has converged

fitintercept

Fit Intercept

Whether to calculate the intercept for this model. The intercept is not treated as a probabilistic parameter and thus has no associated variance. If set to False, no intercept will be used in calculations

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.

compute_score

Compute Score

If True, compute the log marginal likelihood at each iteration of the optimization.