Visibility: public Uploaded 18-09-2020 by Marcos de Paula Bueno sklearn==0.23.2 numpy>=1.6.1 scipy>=0.9 0 runs
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  • openml-python python scikit-learn sklearn sklearn_0.23.2
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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', 'saga' and 'newton-cg' solvers.) This class implements regularized logistic regression using the 'liblinear' library, 'newton-cg', 'sag', 'saga' and 'lbfgs' solvers. Note that regularization is applied by default. 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, or no regularization. The 'liblinear' solver supports both L1 and L2 regularization, with a dual formulation only for the L2 penalty. The Elastic-Net regularization is only su...


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'*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
l1_ratioThe Elastic-Net mixing parameter, with ``0 <= l1_ratio <= 1``. Only used if ``penalty='elasticnet'``. Setting ``l1_ratio=0`` is equivalent to using ``penalty='l2'``, while setting ``l1_ratio=1`` is equivalent to using ``penalty='l1'``. For ``0 < l1_ratio <1``, the penalty is a combination of L1 and L2.default: null
max_iterMaximum number of iterations taken for the solvers to converge multi_class : {'auto', 'ovr', 'multinomial'}, default='auto' If 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:: 0.22 Default changed from 'ovr' to 'auto' in 0.22default: 100
multi_classdefault: "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 detailsdefault: null
penaltydefault: "l2"
random_stateUsed when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the data. See :term:`Glossary ` for details solver : {'newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'}, default='lbfgs' Algorithm 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', 'sag' and 'saga' handle L2 or no penalty - 'liblinear' and 'saga' also handle L1 penalty - 'saga' also supports 'elasticnet' penalty - 'liblinear' does not support setting ``penalty='none'`` 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 Gr...default: null
solverdefault: "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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