SkLearn Lasso Regression

In Lasso Regression, Linear Model trained with L1 prior as regularizer.

Type

ml-estimator

Class

fire.nodes.sklearn.NodeSklearnLassoRegression

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 L1 term. Defaults to 1.0. Alpha = 0 is equivalent to an ordinary least square, solved by the LinearRegression object.

fit_intercept

Fit Intercept

Whether to calculate the intercept for this model. 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.

precompute

Precompute

Whether to use a precomputed Gram matrix to speed up calculations.

max_iter

Max Iterations

The maximum number of iterations.

tol

Tolerance

The tolerance for the optimization: if the updates are smaller than tol, the optimization code checks the dual gap for optimality and continues until it is smaller than tol.

warm_start

Warm Start

When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution.

positive

Positive

When set to True, forces the coefficients to be positive.

random_state

Random State

The seed of the pseudo random number generator that selects a random feature to update.

selection

Selection

If set to ‘random’, a random coefficient is updated every iteration rather than looping over features sequentially by default. This (setting to ‘random’) often leads to significantly faster convergence especially when tol is higher than 1e-4.