tick.linear_model.LogisticRegression¶

class tick.linear_model.LogisticRegression(fit_intercept=True, penalty='l2', C=1000.0, solver='svrg', step=None, tol=1e-05, max_iter=100, verbose=False, warm_start=False, print_every=10, record_every=10, sdca_ridge_strength=0.001, elastic_net_ratio=0.95, random_state=None, blocks_start=None, blocks_length=None)[source]¶

Logistic regression learner, with many choices of penalization and solvers.

Parameters:

C : float, default=1e3

Level of penalization

penalty : {‘l1’, ‘l2’, ‘elasticnet’, ‘tv’, ‘none’, ‘binarsity’}, default=’l2’

The penalization to use. Default is ridge penalization

solver : {‘gd’, ‘agd’, ‘bfgs’, ‘svrg’, ‘sdca’, ‘sgd’}, default=’svrg’

The name of the solver to use

fit_intercept : bool, default=True

If True, include an intercept in the model

warm_start : bool, default=False

If true, learning will start from the last reached solution

step : float, default=None

Initial step size used for learning. Used in gd, agd, sgd and svrg solvers

tol : float, default=1e-5

The tolerance of the solver (iterations stop when the stopping criterion is below it). By default the solver does max_iter iterations

max_iter : int, default=100

Maximum number of iterations of the solver

verbose : bool, default=False

If True, we verbose things, otherwise the solver does not print anything (but records information in history anyway)

print_every : int, default=10

Print history information when n_iter (iteration number) is a multiple of print_every

record_every : int, default=10

Record history information when n_iter (iteration number) is a multiple of record_every

Attributes:

weights : numpy.array, shape=(n_features,)

The learned weights of the model (not including the intercept)

intercept : float or None

The intercept, if fit_intercept=True, otherwise None

classes : numpy.array, shape=(n_classes,)

The class labels of our problem

Examples using tick.linear_model.LogisticRegression¶