.. _`Logistic Regression`: .. _`org.sysess.sympathy.machinelearning.logisticregression`: Logistic Regression ~~~~~~~~~~~~~~~~~~~ .. image:: logistic_regression.svg :width: 48 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. .. versionadded:: 0.19 l1 penalty with SAGA solver (allowing 'multinomial' + L1) - *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. .. versionadded:: 0.17 *class_weight='balanced'* - *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. Does not work for liblinear solver. .. versionadded:: 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* default: 'liblinear' Algorithm to use in the optimization problem. - 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' is limited to one-versus-rest schemes. - 'newton-cg', 'lbfgs' and 'sag' only handle L2 penalty, whereas 'liblinear' and 'saga' handle L1 penalty. Note that 'sag' and 'saga' fast convergence is only guaranteed on features with approximately the same scale. You can preprocess the data with a scaler from sklearn.preprocessing. .. versionadded:: 0.17 Stochastic Average Gradient descent solver. .. versionadded:: 0.19 SAGA solver. - *n_jobs* Number of CPU cores used when parallelizing over classes if multi_class="ovr". Ignored when the solver is set to "liblinear" regardless of multi_class. If given -1 then all cores are used - *random_state* The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. Used when ``solver`` == 'sag' or '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. .. versionadded:: 0.17 *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* 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. `coef_` is of shape (1, n_features) when the given problem is binary. - *intercept_* Intercept (a.k.a. bias) added to the decision function. If `fit_intercept` is set to False, the intercept is set to zero. `intercept_` is of shape(1,) when the problem is binary. :Inputs: :Outputs: **model** : model Model *Ports*: **Outputs**: :model: model Model *Configuration*: **penalty** Used to specify the norm used in the penalization. The 'newton-cg', 'sag' and 'lbfgs' solvers support only l2 penalties. .. versionadded:: 0.19 l1 penalty with SAGA solver (allowing 'multinomial' + L1) **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. .. versionadded:: 0.17 *class_weight='balanced'* **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. Does not work for liblinear solver. .. versionadded:: 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** default: 'liblinear' Algorithm to use in the optimization problem. - 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' is limited to one-versus-rest schemes. - 'newton-cg', 'lbfgs' and 'sag' only handle L2 penalty, whereas 'liblinear' and 'saga' handle L1 penalty. Note that 'sag' and 'saga' fast convergence is only guaranteed on features with approximately the same scale. You can preprocess the data with a scaler from sklearn.preprocessing. .. versionadded:: 0.17 Stochastic Average Gradient descent solver. .. versionadded:: 0.19 SAGA solver. **n_jobs** Number of CPU cores used when parallelizing over classes if multi_class="ovr". Ignored when the solver is set to "liblinear" regardless of multi_class. If given -1 then all cores are used **random_state** The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. Used when ``solver`` == 'sag' or '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. .. versionadded:: 0.17 *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers. .. automodule:: node_regression .. class:: LogisticRegression