skglm.SparseLogisticRegression

class skglm.SparseLogisticRegression(alpha=1.0, l1_ratio=1.0, tol=0.0001, max_iter=20, max_epochs=1000, verbose=0, fit_intercept=True, warm_start=False)

Sparse Logistic regression estimator.

The optimization objective for sparse Logistic regression is:

`1 / n_"samples" sum_(i=1)^(n_"samples") log(1 + exp(-y_i x_i^T w)) + alpha ||w||_1`

Parameters:

alphafloat, default=1.0

Regularization strength; must be a positive float.

l1_ratiofloat, default=1.0

The ElasticNet mixing parameter, with 0 <= l1_ratio <= 1. For l1_ratio = 0 the penalty is an L2 penalty. For l1_ratio = 1 it is an L1 penalty. For 0 < l1_ratio < 1, the penalty is a combination of L1 and L2.

tolfloat, optional

Stopping criterion for the optimization.

max_iterint, optional

The maximum number of outer iterations (subproblem definitions).

max_epochsint

Maximum number of prox Newton iterations on each subproblem.

verbosebool or int

Amount of verbosity.

fit_interceptbool, optional (default=True)

Whether or not to fit an intercept.

warm_startbool, optional (default=False)

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

Attributes:

classes_ndarray, shape (n_classes, )

A list of class labels known to the classifier.

coef_ndarray, shape (1, n_features) or (n_classes, n_features)

Coefficient of the features in the decision function.

coef_ is of shape (1, n_features) when the given problem is binary.

intercept_ndarray, shape (1,) or (n_classes,)

constant term in decision function. Not handled yet.

n_iter_int

Number of subproblems solved to reach the specified tolerance.

__init__(alpha=1.0, l1_ratio=1.0, tol=0.0001, max_iter=20, max_epochs=1000, verbose=0, fit_intercept=True, warm_start=False)

Methods

__init__([alpha, l1_ratio, tol, max_iter, ...])
decision_function(X)	Predict confidence scores for samples.
densify()	Convert coefficient matrix to dense array format.
fit(X, y)	Fit the model according to the given training data.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
predict(X)	Predict class labels for samples in X.
predict_proba(X)	Probability estimates.
score(X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
set_params(**params)	Set the parameters of this estimator.
set_score_request(*[, sample_weight])	Request metadata passed to the score method.
sparsify()	Convert coefficient matrix to sparse format.