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Logistic Regression (aka logit, MaxEnt) classifier. This node has been automatically generated by wrapping the ``sklearn.linear_model.logistic.LogisticRegression`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. In the multiclass case, the training algorithm uses the onevsrest (OvR) scheme if the 'multi_class' option is set to 'ovr' and uses the crossentropy loss, if the 'multi_class' option is set to 'multinomial'. (Currently the 'multinomial' option is supported only by the 'lbfgs' and 'newtoncg' solvers.) This class implements regularized logistic regression using the `liblinear` library, newtoncg and lbfgs solvers. It can handle both dense and sparse input. Use Cordered arrays or CSR matrices containing 64bit floats for optimal performance; any other input format will be converted (and copied). The newtoncg 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. Read more in the :ref:`User Guide <logistic_regression>`. **Parameters** 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. C : float, optional (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 if solver is liblinear. when self.fit_intercept is True, instance vector 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. 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'* instead of deprecated *class_weight='auto'*. max_iter : int Useful only for the newtoncg, sag and lbfgs solvers. Maximum number of iterations taken for the solvers to converge. random_state : int seed, RandomState instance, or None (default) The seed of the pseudo random number generator to use when shuffling the data. 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. 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. .. versionadded:: 0.17 Stochastic Average Gradient descent solver. tol : float, optional Tolerance for stopping criteria. 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' solver. verbose : int For the liblinear and lbfgs solvers set verbose to any positive number for verbosity. warm_start : bool, optional 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*, *newtoncg*, *sag* solvers. n_jobs : int, optional Number of CPU cores used during the crossvalidation loop. If given a value of 1, all cores are used. **Attributes** ``coef_`` : array, shape (n_classes, n_features) Coefficient of the features in the decision function. ``intercept_`` : array, shape (n_classes,) Intercept (a.k.a. bias) added to the decision function. If `fit_intercept` is set to False, the intercept is set to zero. ``n_iter_`` : array, shape (n_classes,) or (1, ) 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. See also SGDClassifier : incrementally trained logistic regression (when given the parameter ``loss="log"``). sklearn.svm.LinearSVC : learns SVM models using the same algorithm. **Notes** The underlying C implementation uses a random number generator to select features when fitting the model. It is thus not uncommon, to have slightly different results for the same input data. If that happens, try with a smaller tol parameter. Predict output may not match that of standalone liblinear in certain cases. See :ref:`differences from liblinear <liblinear_differences>` in the narrative documentation. **References** LIBLINEAR  A Library for Large Linear Classification http://www.csie.ntu.edu.tw/~cjlin/liblinear/ HsiangFu Yu, FangLan Huang, ChihJen Lin (2011). Dual coordinate descent methods for logistic regression and maximum entropy models. Machine Learning 85(12):4175. http://www.csie.ntu.edu.tw/~cjlin/papers/maxent_dual.pdf














Inherited from Inherited from Inherited from 

Inherited from ClassifierCumulator  







Inherited from ClassifierNode  










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_train_seq List of tuples: 

dtype dtype 

input_dim Input dimensions 

output_dim Output dimensions 

supported_dtypes Supported dtypes 

Logistic Regression (aka logit, MaxEnt) classifier. This node has been automatically generated by wrapping the ``sklearn.linear_model.logistic.LogisticRegression`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. In the multiclass case, the training algorithm uses the onevsrest (OvR) scheme if the 'multi_class' option is set to 'ovr' and uses the crossentropy loss, if the 'multi_class' option is set to 'multinomial'. (Currently the 'multinomial' option is supported only by the 'lbfgs' and 'newtoncg' solvers.) This class implements regularized logistic regression using the `liblinear` library, newtoncg and lbfgs solvers. It can handle both dense and sparse input. Use Cordered arrays or CSR matrices containing 64bit floats for optimal performance; any other input format will be converted (and copied). The newtoncg 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. Read more in the :ref:`User Guide <logistic_regression>`. **Parameters** 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. C : float, optional (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 if solver is liblinear. when self.fit_intercept is True, instance vector 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. 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'* instead of deprecated *class_weight='auto'*. max_iter : int Useful only for the newtoncg, sag and lbfgs solvers. Maximum number of iterations taken for the solvers to converge. random_state : int seed, RandomState instance, or None (default) The seed of the pseudo random number generator to use when shuffling the data. 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. 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. .. versionadded:: 0.17 Stochastic Average Gradient descent solver. tol : float, optional Tolerance for stopping criteria. 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' solver. verbose : int For the liblinear and lbfgs solvers set verbose to any positive number for verbosity. warm_start : bool, optional 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*, *newtoncg*, *sag* solvers. n_jobs : int, optional Number of CPU cores used during the crossvalidation loop. If given a value of 1, all cores are used. **Attributes** ``coef_`` : array, shape (n_classes, n_features) Coefficient of the features in the decision function. ``intercept_`` : array, shape (n_classes,) Intercept (a.k.a. bias) added to the decision function. If `fit_intercept` is set to False, the intercept is set to zero. ``n_iter_`` : array, shape (n_classes,) or (1, ) 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. See also SGDClassifier : incrementally trained logistic regression (when given the parameter ``loss="log"``). sklearn.svm.LinearSVC : learns SVM models using the same algorithm. **Notes** The underlying C implementation uses a random number generator to select features when fitting the model. It is thus not uncommon, to have slightly different results for the same input data. If that happens, try with a smaller tol parameter. Predict output may not match that of standalone liblinear in certain cases. See :ref:`differences from liblinear <liblinear_differences>` in the narrative documentation. **References** LIBLINEAR  A Library for Large Linear Classification http://www.csie.ntu.edu.tw/~cjlin/liblinear/ HsiangFu Yu, FangLan Huang, ChihJen Lin (2011). Dual coordinate descent methods for logistic regression and maximum entropy models. Machine Learning 85(12):4175. http://www.csie.ntu.edu.tw/~cjlin/papers/maxent_dual.pdf



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.LogisticRegression 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.LogisticRegression`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. **Parameters** X : {arraylike, sparse matrix}, shape (n_samples, n_features) Training vector, where n_samples in the number of samples and n_features is the number of features. y : arraylike, shape (n_samples,) Target vector relative to X. sample_weight : arraylike, shape (n_samples,) optional Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight. .. versionadded:: 0.17 *sample_weight* support to LogisticRegression. Returns self : object Returns self.

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