Logistic Regression¶

Some of the docstrings for this module have been automatically extracted from the scikit-learn library and are covered by their respective licenses.

class node_regression.LogisticRegression[source]¶

Logistic regression of a categorical dependent variable

Configuration:	penalty Used to specify the norm used in the penalization. The ‘newton-cg’, ‘sag’ and ‘lbfgs’ solvers support only l2 penalties. dual Dual or primal formulation. Dual formulation is only implemented for l2 penalty with liblinear solver. Prefer dual=False when n_samples > n_features. C Inverse of regularization strength; must be a positive float. Like in support vector machines, smaller values specify stronger regularization. fit_intercept Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function. intercept_scaling 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 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. New in version 0.17: class_weight=’balanced’ instead of deprecated class_weight=’auto’. tol Tolerance for stopping criteria. multi_class 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 ‘newton-cg’, ‘sag’ and ‘lbfgs’ solver. New in version 0.18: Stochastic Average Gradient descent solver for ‘multinomial’ case. max_iter Useful only for the newton-cg, sag and lbfgs solvers. Maximum number of iterations taken for the solvers to converge. solver Algorithm to use in the optimization problem. For small datasets, ‘liblinear’ is a good choice, whereas ‘sag’ is faster for large ones. For multiclass problems, only ‘newton-cg’, ‘sag’ and ‘lbfgs’ handle multinomial loss; ‘liblinear’ is limited to one-versus-rest schemes. ‘newton-cg’, ‘lbfgs’ and ‘sag’ only handle L2 penalty. Note that ‘sag’ fast convergence is only guaranteed on features with approximately the same scale. You can preprocess the data with a scaler from sklearn.preprocessing. New in version 0.17: Stochastic Average Gradient descent solver. n_jobs Number of CPU cores used during the cross-validation loop. If given a value of -1, all cores are used. random_state The seed of the pseudo random number generator to use when shuffling the data. Used only in solvers ‘sag’ and ‘liblinear’. warm_start When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. Useless for liblinear solver. New in version 0.17: warm_start to support lbfgs, newton-cg, sag solvers.
Attributes:	n_iter_ Actual number of iterations for all classes. If binary or multinomial, it returns only 1 element. For liblinear solver, only the maximum number of iteration across all classes is given. coef_ Coefficient of the features in the decision function. intercept_ Intercept (a.k.a. bias) added to the decision function. If fit_intercept is set to False, the intercept is set to zero.
Inputs:
Outputs:	model : model Model