sklearn.linear_model.logistic_regression_path()

X : array-like or sparse matrix, shape (n_samples, n_features)

Input data.

y : array-like, shape (n_samples,)

Input data, target values.

Cs : int | array-like, shape (n_cs,)

List of values for the regularization parameter or integer specifying the number of regularization parameters that should be used. In this case, the parameters will be chosen in a logarithmic scale between 1e-4 and 1e4.

pos_class : int, None

The class with respect to which we perform a one-vs-all fit. If None, then it is assumed that the given problem is binary.

fit_intercept : bool

Whether to fit an intercept for the model. In this case the shape of the returned array is (n_cs, n_features + 1).

max_iter : int

Maximum number of iterations for the solver.

tol : float

Stopping criterion. For the newton-cg and lbfgs solvers, the iteration will stop when max{|g_i | i = 1, ..., n} <= tol where g_i is the i-th component of the gradient.

verbose : int

For the liblinear and lbfgs solvers set verbose to any positive number for verbosity.

solver : {?lbfgs?, ?newton-cg?, ?liblinear?, ?sag?}

Numerical solver to use.

coef : array-like, shape (n_features,), default None

Initialization value for coefficients of logistic regression. Useless for liblinear solver.

copy : bool, default False

Whether or not to produce a copy of the data. A copy is not required anymore. This parameter is deprecated and will be removed in 0.19.

class_weight : dict or ?balanced?, optional

Weights associated with classes in the form {class_label: weight}. 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.

dual : bool

Dual or primal formulation. Dual formulation is only implemented for l2 penalty with liblinear solver. Prefer dual=False when n_samples > n_features.

penalty : str, ?l1? or ?l2?

Used to specify the norm used in the penalization. The ?newton-cg?, ?sag? and ?lbfgs? solvers support only l2 penalties.

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.

multi_class : str, {?ovr?, ?multinomial?}

Multiclass option can be either ?ovr? or ?multinomial?. If the option chosen is ?ovr?, then a binary problem is fit for each label. Else the loss minimised is the multinomial loss fit across the entire probability distribution. Works only for the ?lbfgs? and ?newton-cg? solvers.

random_state : int seed, RandomState instance, or None (default)

The seed of the pseudo random number generator to use when shuffling the data. Used only in solvers ?sag? and ?liblinear?.

check_input : bool, default True

If False, the input arrays X and y will not be checked.

max_squared_sum : float, default None

Maximum squared sum of X over samples. Used only in SAG solver. If None, it will be computed, going through all the samples. The value should be precomputed to speed up cross validation.

sample_weight : array-like, shape(n_samples,) optional

Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight.