{"flow":{"id":"18686","uploader":"11601","name":"sklearn.linear_model._logistic.LogisticRegression","custom_name":"sklearn.LogisticRegression","class_name":"sklearn.linear_model._logistic.LogisticRegression","version":"4","external_version":"openml==0.10.2,sklearn==0.23.2","description":"Logistic Regression (aka logit, MaxEnt) classifier.\n\nIn the multiclass case, the training algorithm uses the one-vs-rest (OvR)\nscheme if the 'multi_class' option is set to 'ovr', and uses the\ncross-entropy loss if the 'multi_class' option is set to 'multinomial'.\n(Currently the 'multinomial' option is supported only by the 'lbfgs',\n'sag', 'saga' and 'newton-cg' solvers.)\n\nThis class implements regularized logistic regression using the\n'liblinear' library, 'newton-cg', 'sag', 'saga' and 'lbfgs' solvers. **Note\nthat regularization is applied by default**. It can handle both dense\nand sparse input. Use C-ordered arrays or CSR matrices containing 64-bit\nfloats for optimal performance; any other input format will be converted\n(and copied).\n\nThe 'newton-cg', 'sag', and 'lbfgs' solvers support only L2 regularization\nwith primal formulation, or no regularization. The 'liblinear' solver\nsupports both L1 and L2 regularization, with a dual formulation only for\nthe L2 penalty. The Elastic-Net regularization is only su...","upload_date":"2020-09-18T03:11:00","language":"English","dependencies":"sklearn==0.23.2\nnumpy>=1.6.1\nscipy>=0.9","parameter":[{"name":"C","data_type":"float","default_value":"1.0","description":"Inverse of regularization strength; must be a positive float\n Like in support vector machines, smaller values specify stronger\n regularization"},{"name":"class_weight","data_type":"dict or","default_value":"null","description":"Weights associated with classes in the form ``{class_label: weight}``\n If not given, all classes are supposed to have weight one\n\n The \"balanced\" mode uses the values of y to automatically adjust\n weights inversely proportional to class frequencies in the input data\n as ``n_samples \/ (n_classes * np.bincount(y))``\n\n Note that these weights will be multiplied with sample_weight (passed\n through the fit method) if sample_weight is specified\n\n .. versionadded:: 0.17\n *class_weight='balanced'*"},{"name":"dual","data_type":"bool","default_value":"false","description":"Dual or primal formulation. Dual formulation is only implemented for\n l2 penalty with liblinear solver. Prefer dual=False when\n n_samples > n_features"},{"name":"fit_intercept","data_type":"bool","default_value":"true","description":"Specifies if a constant (a.k.a. bias or intercept) should be\n added to the decision function"},{"name":"intercept_scaling","data_type":"float","default_value":"1","description":"Useful only when the solver 'liblinear' is used\n and self.fit_intercept is set to True. In this case, x becomes\n [x, self.intercept_scaling],\n i.e. a \"synthetic\" feature with constant value equal to\n intercept_scaling is appended to the instance vector\n The intercept becomes ``intercept_scaling * synthetic_feature_weight``\n\n Note! the synthetic feature weight is subject to l1\/l2 regularization\n as all other features\n To lessen the effect of regularization on synthetic feature weight\n (and therefore on the intercept) intercept_scaling has to be increased"},{"name":"l1_ratio","data_type":"float","default_value":"null","description":"The Elastic-Net mixing parameter, with ``0 <= l1_ratio <= 1``. Only\n used if ``penalty='elasticnet'``. Setting ``l1_ratio=0`` is equivalent\n to using ``penalty='l2'``, while setting ``l1_ratio=1`` is equivalent\n to using ``penalty='l1'``. For ``0 < l1_ratio <1``, the penalty is a\n combination of L1 and L2."},{"name":"max_iter","data_type":"int","default_value":"100","description":"Maximum number of iterations taken for the solvers to converge\n\nmulti_class : {'auto', 'ovr', 'multinomial'}, default='auto'\n If the option chosen is 'ovr', then a binary problem is fit for each\n label. For 'multinomial' the loss minimised is the multinomial loss fit\n across the entire probability distribution, *even when the data is\n binary*. 'multinomial' is unavailable when solver='liblinear'\n 'auto' selects 'ovr' if the data is binary, or if solver='liblinear',\n and otherwise selects 'multinomial'\n\n .. versionadded:: 0.18\n Stochastic Average Gradient descent solver for 'multinomial' case\n .. versionchanged:: 0.22\n Default changed from 'ovr' to 'auto' in 0.22"},{"name":"multi_class","data_type":[],"default_value":"\"auto\"","description":[]},{"name":"n_jobs","data_type":"int","default_value":"null","description":"Number of CPU cores used when parallelizing over classes if\n multi_class='ovr'\". This parameter is ignored when the ``solver`` is\n set to 'liblinear' regardless of whether 'multi_class' is specified or\n not. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`\n context. ``-1`` means using all processors\n See :term:`Glossary ` for more details"},{"name":"penalty","data_type":[],"default_value":"\"l2\"","description":[]},{"name":"random_state","data_type":"int","default_value":"null","description":"Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the\n data. See :term:`Glossary ` for details\n\nsolver : {'newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'}, default='lbfgs'\n\n Algorithm to use in the optimization problem\n\n - For small datasets, 'liblinear' is a good choice, whereas 'sag' and\n 'saga' are faster for large ones\n - For multiclass problems, only 'newton-cg', 'sag', 'saga' and 'lbfgs'\n handle multinomial loss; 'liblinear' is limited to one-versus-rest\n schemes\n - 'newton-cg', 'lbfgs', 'sag' and 'saga' handle L2 or no penalty\n - 'liblinear' and 'saga' also handle L1 penalty\n - 'saga' also supports 'elasticnet' penalty\n - 'liblinear' does not support setting ``penalty='none'``\n\n Note that 'sag' and 'saga' fast convergence is only guaranteed on\n features with approximately the same scale. You can\n preprocess the data with a scaler from sklearn.preprocessing\n\n .. versionadded:: 0.17\n Stochastic Average Gr..."},{"name":"solver","data_type":[],"default_value":"\"lbfgs\"","description":[]},{"name":"tol","data_type":"float","default_value":"0.0001","description":"Tolerance for stopping criteria"},{"name":"verbose","data_type":"int","default_value":"0","description":"For the liblinear and lbfgs solvers set verbose to any positive\n number for verbosity"},{"name":"warm_start","data_type":"bool","default_value":"false","description":"When set to True, reuse the solution of the previous call to fit as\n initialization, otherwise, just erase the previous solution\n Useless for liblinear solver. See :term:`the Glossary `\n\n .. versionadded:: 0.17\n *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers"}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.23.2"]}}