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Logistic Regression CV (aka logit, MaxEnt) classifier. This node has been automatically generated by wrapping the ``sklearn.linear_model.logistic.LogisticRegressionCV`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. This class implements logistic regression using liblinear, newtoncg, sag of lbfgs optimizer. The newtoncg, sag and lbfgs solvers support only L2 regularization with primal formulation. The liblinear solver supports both L1 and L2 regularization, with a dual formulation only for the L2 penalty. For the grid of Cs values (that are set by default to be ten values in a logarithmic scale between 1e4 and 1e4), the best hyperparameter is selected by the crossvalidator StratifiedKFold, but it can be changed using the cv parameter. In the case of newtoncg and lbfgs solvers, we warm start along the path i.e guess the initial coefficients of the present fit to be the coefficients got after convergence in the previous fit, so it is supposed to be faster for highdimensional dense data. For a multiclass problem, the hyperparameters for each class are computed using the best scores got by doing a onevsrest in parallel across all folds and classes. Hence this is not the true multinomial loss. Read more in the :ref:`User Guide <logistic_regression>`. **Parameters** Cs : list of floats  int Each of the values in Cs describes the inverse of regularization strength. If Cs is as an int, then a grid of Cs values are chosen in a logarithmic scale between 1e4 and 1e4. 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. 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. .. versionadded:: 0.17 class_weight == 'balanced' cv : integer or crossvalidation generator The default crossvalidation generator used is Stratified KFolds. If an integer is provided, then it is the number of folds used. See the module :mod:`sklearn.cross_validation` module for the list of possible crossvalidation objects. penalty : str, 'l1' or 'l2' Used to specify the norm used in the penalization. The newtoncg and lbfgs solvers support only l2 penalties. 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. scoring : callabale Scoring function to use as crossvalidation criteria. For a list of scoring functions that can be used, look at :mod:`sklearn.metrics`. The default scoring option used is accuracy_score. solver : {'newtoncg', 'lbfgs', 'liblinear', 'sag'} 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 'newtoncg' and 'lbfgs' handle multinomial loss; 'sag' and 'liblinear' are limited to oneversusrest schemes.  'newtoncg', 'lbfgs' and 'sag' only handle L2 penalty.  'liblinear' might be slower in LogisticRegressionCV because it does not handle warmstarting. tol : float, optional Tolerance for stopping criteria. max_iter : int, optional Maximum number of iterations of the optimization algorithm. n_jobs : int, optional Number of CPU cores used during the crossvalidation loop. If given a value of 1, all cores are used. verbose : int For the 'liblinear', 'sag' and 'lbfgs' solvers set verbose to any positive number for verbosity. refit : bool If set to True, the scores are averaged across all folds, and the coefs and the C that corresponds to the best score is taken, and a final refit is done using these parameters. Otherwise the coefs, intercepts and C that correspond to the best scores across folds are averaged. 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 'lbfgs' and 'newtoncg' solvers. intercept_scaling : float, default 1. Useful only if solver is liblinear. This parameter is 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 equals 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. random_state : int seed, RandomState instance, or None (default) The seed of the pseudo random number generator to use when shuffling the data. **Attributes** ``coef_`` : array, shape (1, n_features) or (n_classes, n_features) Coefficient of the features in the decision function. `coef_` is of shape (1, n_features) when the given problem is binary. `coef_` is readonly property derived from `raw_coef_` that follows the internal memory layout of liblinear. ``intercept_`` : array, shape (1,) or (n_classes,) Intercept (a.k.a. bias) added to the decision function. It is available only when parameter intercept is set to True and is of shape(1,) when the problem is binary. ``Cs_`` : array Array of C i.e. inverse of regularization parameter values used for crossvalidation. ``coefs_paths_`` : array, shape ``(n_folds, len(Cs_), n_features)`` or ``(n_folds, len(Cs_), n_features + 1)`` dict with classes as the keys, and the path of coefficients obtained during crossvalidating across each fold and then across each Cs after doing an OvR for the corresponding class as values. If the 'multi_class' option is set to 'multinomial', then the coefs_paths are the coefficients corresponding to each class. Each dict value has shape ``(n_folds, len(Cs_), n_features)`` or ``(n_folds, len(Cs_), n_features + 1)`` depending on whether the intercept is fit or not. ``scores_`` : dict dict with classes as the keys, and the values as the grid of scores obtained during crossvalidating each fold, after doing an OvR for the corresponding class. If the 'multi_class' option given is 'multinomial' then the same scores are repeated across all classes, since this is the multinomial class. Each dict value has shape (n_folds, len(Cs)) ``C_`` : array, shape (n_classes,) or (n_classes  1,) Array of C that maps to the best scores across every class. If refit is set to False, then for each class, the best C is the average of the C's that correspond to the best scores for each fold. ``n_iter_`` : array, shape (n_classes, n_folds, n_cs) or (1, n_folds, n_cs) Actual number of iterations for all classes, folds and Cs. In the binary or multinomial cases, the first dimension is equal to 1. See also LogisticRegression














Inherited from Inherited from Inherited from 

Inherited from ClassifierCumulator  







Inherited from ClassifierNode  










Inherited from Node  




























































Inherited from 

Inherited from Node  

_train_seq List of tuples: 

dtype dtype 

input_dim Input dimensions 

output_dim Output dimensions 

supported_dtypes Supported dtypes 

Logistic Regression CV (aka logit, MaxEnt) classifier. This node has been automatically generated by wrapping the ``sklearn.linear_model.logistic.LogisticRegressionCV`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. This class implements logistic regression using liblinear, newtoncg, sag of lbfgs optimizer. The newtoncg, sag and lbfgs solvers support only L2 regularization with primal formulation. The liblinear solver supports both L1 and L2 regularization, with a dual formulation only for the L2 penalty. For the grid of Cs values (that are set by default to be ten values in a logarithmic scale between 1e4 and 1e4), the best hyperparameter is selected by the crossvalidator StratifiedKFold, but it can be changed using the cv parameter. In the case of newtoncg and lbfgs solvers, we warm start along the path i.e guess the initial coefficients of the present fit to be the coefficients got after convergence in the previous fit, so it is supposed to be faster for highdimensional dense data. For a multiclass problem, the hyperparameters for each class are computed using the best scores got by doing a onevsrest in parallel across all folds and classes. Hence this is not the true multinomial loss. Read more in the :ref:`User Guide <logistic_regression>`. **Parameters** Cs : list of floats  int Each of the values in Cs describes the inverse of regularization strength. If Cs is as an int, then a grid of Cs values are chosen in a logarithmic scale between 1e4 and 1e4. 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. 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. .. versionadded:: 0.17 class_weight == 'balanced' cv : integer or crossvalidation generator The default crossvalidation generator used is Stratified KFolds. If an integer is provided, then it is the number of folds used. See the module :mod:`sklearn.cross_validation` module for the list of possible crossvalidation objects. penalty : str, 'l1' or 'l2' Used to specify the norm used in the penalization. The newtoncg and lbfgs solvers support only l2 penalties. 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. scoring : callabale Scoring function to use as crossvalidation criteria. For a list of scoring functions that can be used, look at :mod:`sklearn.metrics`. The default scoring option used is accuracy_score. solver : {'newtoncg', 'lbfgs', 'liblinear', 'sag'} 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 'newtoncg' and 'lbfgs' handle multinomial loss; 'sag' and 'liblinear' are limited to oneversusrest schemes.  'newtoncg', 'lbfgs' and 'sag' only handle L2 penalty.  'liblinear' might be slower in LogisticRegressionCV because it does not handle warmstarting. tol : float, optional Tolerance for stopping criteria. max_iter : int, optional Maximum number of iterations of the optimization algorithm. n_jobs : int, optional Number of CPU cores used during the crossvalidation loop. If given a value of 1, all cores are used. verbose : int For the 'liblinear', 'sag' and 'lbfgs' solvers set verbose to any positive number for verbosity. refit : bool If set to True, the scores are averaged across all folds, and the coefs and the C that corresponds to the best score is taken, and a final refit is done using these parameters. Otherwise the coefs, intercepts and C that correspond to the best scores across folds are averaged. 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 'lbfgs' and 'newtoncg' solvers. intercept_scaling : float, default 1. Useful only if solver is liblinear. This parameter is 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 equals 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. random_state : int seed, RandomState instance, or None (default) The seed of the pseudo random number generator to use when shuffling the data. **Attributes** ``coef_`` : array, shape (1, n_features) or (n_classes, n_features) Coefficient of the features in the decision function. `coef_` is of shape (1, n_features) when the given problem is binary. `coef_` is readonly property derived from `raw_coef_` that follows the internal memory layout of liblinear. ``intercept_`` : array, shape (1,) or (n_classes,) Intercept (a.k.a. bias) added to the decision function. It is available only when parameter intercept is set to True and is of shape(1,) when the problem is binary. ``Cs_`` : array Array of C i.e. inverse of regularization parameter values used for crossvalidation. ``coefs_paths_`` : array, shape ``(n_folds, len(Cs_), n_features)`` or ``(n_folds, len(Cs_), n_features + 1)`` dict with classes as the keys, and the path of coefficients obtained during crossvalidating across each fold and then across each Cs after doing an OvR for the corresponding class as values. If the 'multi_class' option is set to 'multinomial', then the coefs_paths are the coefficients corresponding to each class. Each dict value has shape ``(n_folds, len(Cs_), n_features)`` or ``(n_folds, len(Cs_), n_features + 1)`` depending on whether the intercept is fit or not. ``scores_`` : dict dict with classes as the keys, and the values as the grid of scores obtained during crossvalidating each fold, after doing an OvR for the corresponding class. If the 'multi_class' option given is 'multinomial' then the same scores are repeated across all classes, since this is the multinomial class. Each dict value has shape (n_folds, len(Cs)) ``C_`` : array, shape (n_classes,) or (n_classes  1,) Array of C that maps to the best scores across every class. If refit is set to False, then for each class, the best C is the average of the C's that correspond to the best scores for each fold. ``n_iter_`` : array, shape (n_classes, n_folds, n_cs) or (1, n_folds, n_cs) Actual number of iterations for all classes, folds and Cs. In the binary or multinomial cases, the first dimension is equal to 1. See also LogisticRegression



Transform the data and labels lists to array objects and reshape them.



Predict class labels for samples in X. This node has been automatically generated by wrapping the sklearn.linear_model.logistic.LogisticRegressionCV class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
Returns

Fit the model according to the given training data. This node has been automatically generated by wrapping the sklearn.linear_model.logistic.LogisticRegressionCV class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
Returns

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