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sklearn.linear_model.logistic.LogisticRegression

sklearn.linear_model.logistic.LogisticRegression

Visibility: public Uploaded 13-08-2021 by Sergey Redyuk sklearn==0.18 numpy>=1.6.1 scipy>=0.9 4 runs
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  • openml-python python scikit-learn sklearn sklearn_0.18
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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: 1.0
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'* instead of deprecated *class_weight='auto'*default: null
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: 100
multi_classMulticlass 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 'newton-cg', 'sag' and 'lbfgs' solver .. versionadded:: 0.18 Stochastic Average Gradient descent solver for 'multinomial' casedefault: "ovr"
n_jobsNumber of CPU cores used during the cross-validation loop. If given a value of -1, all cores are used.default: 1
penaltyUsed to specify the norm used in the penalization. The 'newton-cg', 'sag' and 'lbfgs' solvers support only l2 penaltiesdefault: "l2"
random_stateThe seed of the pseudo random number generator to use when shuffling the data. Used only in solvers 'sag' and 'liblinear' solver : {'newton-cg', 'lbfgs', 'liblinear', 'sag'}, default: 'liblinear' 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 'newton-cg', 'sag' and 'lbfgs' handle multinomial loss; 'liblinear' is limited to one-versus-rest schemes - 'newton-cg', '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 solverdefault: 13352
solverdefault: "liblinear"
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 .. versionadded:: 0.17 *warm_start* to support *lbfgs*, *newton-cg*, *sag* solversdefault: false

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