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sklearn.linear_model._ridge.RidgeClassifier

sklearn.linear_model._ridge.RidgeClassifier

Visibility: public Uploaded 13-12-2019 by Evan Peterson sklearn==0.22 numpy>=1.6.1 scipy>=0.9 0 runs
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  • openml-python python scikit-learn sklearn sklearn_0.22
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Classifier using Ridge regression. This classifier first converts the target values into ``{-1, 1}`` and then treats the problem as a regression task (multi-output regression in the multiclass case).

Parameters

alphaRegularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization Alpha corresponds to ``C^-1`` in other linear models such as LogisticRegression or LinearSVCdefault: 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))`` solver : {'auto', 'svd', 'cholesky', 'lsqr', 'sparse_cg', 'sag', 'saga'} Solver to use in the computational routines: - 'auto' chooses the solver automatically based on the type of data - 'svd' uses a Singular Value Decomposition of X to compute the Ridge coefficients. More stable for singular matrices than 'cholesky' - 'cholesky' uses the standard scipy.linalg.solve function to obtain a closed-form solution - 'sparse_cg' uses the conjugate gradient solver as found in scipy.sparse.linalg.cg. As an iterative algorithm, this solver is more appropriate than 'cholesky' for large-scale data (possibility to set `tol` and `max_iter...default: null
copy_XIf True, X will be copied; else, it may be overwrittendefault: true
fit_interceptWhether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered)default: true
max_iterMaximum number of iterations for conjugate gradient solver The default value is determined by scipy.sparse.linalgdefault: null
normalizeThis parameter is ignored when ``fit_intercept`` is set to False If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm If you wish to standardize, please use :class:`sklearn.preprocessing.StandardScaler` before calling ``fit`` on an estimator with ``normalize=False``default: false
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'.default: null
solverdefault: "auto"
tolPrecision of the solutiondefault: 0.001

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