[FIXED] Why is random_state required for ridge & lasso regression in sklearn?

Issue

In scikit-learn, Lasso and Ridge regression are two regression methods which have a random_state attribute. Why do these two methods need this attribute?

From the documentation:

class sklearn.linear_model.Lasso(alpha=1.0, fit_intercept=True, normalize=False, precompute=False, copy_X=True, max_iter=1000, tol=0.0001, warm_start=False, positive=False, random_state=None, selection=’cyclic’)


class sklearn.linear_model.Ridge(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, solver=’auto’, random_state=None)

Solution

In the case of lasso, RNG can be used (depending on other selected options) to randomly select features for which to perform updates:

The seed of the pseudo random number generator that selects a random feature to update. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random. Used when selection == ‘random’.

In the case of ridge, RNG can be used (depending on other selected options) for shuffling data:

The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random. Used when solver == ‘sag’.

Answered By - Dennis Soemers