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sklearn.linear_model.logistic.LogisticRegression

sklearn.linear_model.logistic.LogisticRegression

Visibility: public Uploaded 14-08-2021 by Sergey Redyuk sklearn==0.20.3 numpy>=1.8.2 scipy>=0.13.3 0 runs
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  • openml-python python scikit-learn sklearn sklearn_0.20.3
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Logistic Regression (aka logit, MaxEnt) classifier. In the multiclass case, the training algorithm uses the one-vs-rest (OvR) scheme if the 'multi_class' option is set to 'ovr', and uses the cross- entropy loss if the 'multi_class' option is set to 'multinomial'. (Currently the 'multinomial' option is supported only by the 'lbfgs', 'sag' and 'newton-cg' solvers.) This class implements regularized logistic regression using the 'liblinear' library, 'newton-cg', 'sag' and 'lbfgs' solvers. It can handle both dense and sparse input. Use C-ordered arrays or CSR matrices containing 64-bit floats for optimal performance; any other input format will be converted (and copied). The 'newton-cg', '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.

Parameters

CInverse of regularization strength; must be a positive float Like in support vector machines, smaller values specify stronger regularizationdefault: 0.001
class_weightWeights 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'*default: "balanced"
dualDual or primal formulation. Dual formulation is only implemented for l2 penalty with liblinear solver. Prefer dual=False when n_samples > n_featuresdefault: false
fit_interceptSpecifies if a constant (a.k.a. bias or intercept) should be added to the decision functiondefault: true
intercept_scalingUseful 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 increaseddefault: 1
max_iterUseful only for the newton-cg, sag and lbfgs solvers Maximum number of iterations taken for the solvers to convergedefault: 10000
multi_classIf the option chosen is 'ovr', then a binary problem is fit for each label. For 'multinomial' the loss minimised is the multinomial loss fit across the entire probability distribution, *even when the data is binary*. 'multinomial' is unavailable when solver='liblinear' 'auto' selects 'ovr' if the data is binary, or if solver='liblinear', and otherwise selects 'multinomial' .. versionadded:: 0.18 Stochastic Average Gradient descent solver for 'multinomial' case .. versionchanged:: 0.20 Default will change from 'ovr' to 'auto' in 0.22default: "auto"
n_jobsNumber of CPU cores used when parallelizing over classes if multi_class='ovr'". This parameter is ignored when the ``solver`` is set to 'liblinear' regardless of whether 'multi_class' is specified or not. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors See :term:`Glossary ` for more details.default: null
penaltyUsed 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)default: "l2"
random_stateThe 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'default: null
solverAlgorithm 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 .. versionchanged:: 0.20 Default will change from 'liblinear' to 'lbfgs' in 0.22default: "lbfgs"
tolTolerance for stopping criteriadefault: 0.0001
verboseFor the liblinear and lbfgs solvers set verbose to any positive number for verbositydefault: 0
warm_startWhen set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution Useless for liblinear solver. See :term:`the Glossary ` .. versionadded:: 0.17 *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solversdefault: false

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