LogisticRegression#

class sklearn.linear_model. LogisticRegression ( penalty = 'l2' , * , dual = False , tol = 0.0001 , C = 1.0 , fit_intercept = True , intercept_scaling = 1 , class_weight = None , random_state = None , solver = 'lbfgs' , max_iter = 100 , multi_class = 'deprecated' , verbose = 0 , warm_start = False , n_jobs = None , l1_ratio = None ) [source] #

Logistic Regression (aka logit, MaxEnt) classifier.

In the multiclass case, the training algorithm uses the one-vs-rest (OvR) scheme if the ‘multi_class’ option is set to ‘ovr’, and uses the cross-entropy loss if the ‘multi_class’ option is set to ‘multinomial’. (Currently the ‘multinomial’ option is supported only by the ‘lbfgs’, ‘sag’, ‘saga’ and ‘newton-cg’ solvers.)

This class implements regularized logistic regression using the ‘liblinear’ library, ‘newton-cg’, ‘sag’, ‘saga’ and ‘lbfgs’ solvers. Note that regularization is applied by default. It can handle both dense and sparse input. Use C-ordered arrays or CSR matrices containing 64-bit floats for optimal performance; any other input format will be converted (and copied).

The ‘newton-cg’, ‘sag’, and ‘lbfgs’ solvers support only L2 regularization with primal formulation, or no regularization. The ‘liblinear’ solver supports both L1 and L2 regularization, with a dual formulation only for the L2 penalty. The Elastic-Net regularization is only supported by the ‘saga’ solver.

Read more in the User Guide .

Parameters : penalty , default=’l2’

Specify the norm of the penalty:

• None : no penalty is added;
• 'l2' : add a L2 penalty term and it is the default choice;
• 'l1' : add a L1 penalty term;
• 'elasticnet' : both L1 and L2 penalty terms are added.

Some penalties may not work with some solvers. See the parameter solver below, to know the compatibility between the penalty and solver.

Added in version 0.19: l1 penalty with SAGA solver (allowing ‘multinomial’ + L1)

Dual (constrained) or primal (regularized, see also this equation ) formulation. Dual formulation is only implemented for l2 penalty with liblinear solver. Prefer dual=False when n_samples > n_features.

tol float, default=1e-4

Tolerance for stopping criteria.

C float, default=1.0

Inverse of regularization strength; must be a positive float. Like in support vector machines, smaller values specify stronger regularization.

fit_intercept bool, default=True

Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function.

intercept_scaling float, default=1

Useful only when the solver ‘liblinear’ is used and self.fit_intercept is set to True. In this case, x becomes [x, self.intercept_scaling], i.e. a “synthetic” feature with constant value equal to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic_feature_weight .

Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased.

class_weight dict or ‘balanced’, default=None

Weights associated with classes in the form . If not given, all classes are supposed to have weight one.

The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y)) .

Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified.

Added in version 0.17: class_weight=’balanced’

Used when solver == ‘sag’, ‘saga’ or ‘liblinear’ to shuffle the data. See Glossary for details.

Algorithm to use in the optimization problem. Default is ‘lbfgs’. To choose a solver, you might want to consider the following aspects:

• For small datasets, ‘liblinear’ is a good choice, whereas ‘sag’ and ‘saga’ are faster for large ones;
• For multiclass problems, only ‘newton-cg’, ‘sag’, ‘saga’ and ‘lbfgs’ handle multinomial loss;
• ‘liblinear’ and ‘newton-cholesky’ can only handle binary classification by default. To apply a one-versus-rest scheme for the multiclass setting one can wrapt it with the OneVsRestClassifier .
• ‘newton-cholesky’ is a good choice for n_samples >> n_features , especially with one-hot encoded categorical features with rare categories. Be aware that the memory usage of this solver has a quadratic dependency on n_features because it explicitly computes the Hessian matrix.

The choice of the algorithm depends on the penalty chosen and on (multinomial) multiclass support: