skglm.GeneralizedLinearEstimator

class skglm.GeneralizedLinearEstimator(datafit=None, penalty=None, solver=None)

Generic generalized linear estimator.

This estimator takes a penalty and a datafit and runs a coordinate descent solver to solve the optimization problem. It handles classification and regression tasks.

Parameters:

datafitinstance of BaseDatafit, optional

Datafit. If None, datafit is initialized as a Quadratic datafit. datafit is replaced by a JIT-compiled instance when calling fit.

penaltyinstance of BasePenalty, optional

Penalty. If None, penalty is initialized as a L1 penalty. penalty is replaced by a JIT-compiled instance when calling fit.

solverinstance of BaseSolver, optional

Solver. If None, solver is initialized as an AndersonCD solver.

Attributes:

coef_array, shape (n_features,) or (n_features, n_tasks)

parameter array (`w` in the cost function formula)

sparse_coef_scipy.sparse matrix, shape (n_features, 1) or (n_features, n_tasks)

sparse_coef_ is a readonly property derived from coef_

intercept_array, shape (n_tasks,)

constant term in decision function.

n_iter_int

Number of subproblems solved to reach the specified tolerance.

__init__(datafit=None, penalty=None, solver=None)

Methods

__init__([datafit, penalty, solver])
fit(X, y)	Fit estimator.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters of the estimators including the datafit's and penalty's.
predict(X)	Predict target values for samples in X.
set_params(**params)	Set the parameters of this estimator.