package sklearn
Module Linear_model.RidgeClassifierSource
Source
val create :
?alpha:float ->
?fit_intercept:bool ->
?normalize:bool ->
?copy_X:bool ->
?max_iter:int ->
?tol:float ->
?class_weight:[ `DictIntToFloat of (int * float) list | `Balanced ] ->
?solver:[ `Auto | `Svd | `Cholesky | `Lsqr | `Sparse_cg | `Sag | `Saga ] ->
?random_state:int ->
unit ->
tClassifier 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).
Read more in the :ref:`User Guide <ridge_regression>`.
Parameters ---------- alpha : float, default=1.0 Regularization 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 LinearSVC.
fit_intercept : bool, default=True Whether 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).
normalize : bool, default=False This 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``.
copy_X : bool, default=True If True, X will be copied; else, it may be overwritten.
max_iter : int, default=None Maximum number of iterations for conjugate gradient solver. The default value is determined by scipy.sparse.linalg.
tol : float, default=1e-3 Precision of the solution.
class_weight : dict or 'balanced', default=None Weights 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', default='auto' 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`).
- 'lsqr' uses the dedicated regularized least-squares routine scipy.sparse.linalg.lsqr. It is the fastest and uses an iterative procedure.
- 'sag' uses a Stochastic Average Gradient descent, and 'saga' uses its unbiased and more flexible version named SAGA. Both methods use an iterative procedure, and are often faster than other solvers when both n_samples and n_features are large. 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.
random_state : int, RandomState instance, default=None The 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'.
Attributes ---------- coef_ : ndarray of shape (1, n_features) or (n_classes, n_features) Coefficient of the features in the decision function.
``coef_`` is of shape (1, n_features) when the given problem is binary.
intercept_ : float or ndarray of shape (n_targets,) Independent term in decision function. Set to 0.0 if ``fit_intercept = False``.
n_iter_ : None or ndarray of shape (n_targets,) Actual number of iterations for each target. Available only for sag and lsqr solvers. Other solvers will return None.
classes_ : ndarray of shape (n_classes,) The classes labels.
See Also -------- Ridge : Ridge regression. RidgeClassifierCV : Ridge classifier with built-in cross validation.
Notes ----- For multi-class classification, n_class classifiers are trained in a one-versus-all approach. Concretely, this is implemented by taking advantage of the multi-variate response support in Ridge.
Examples -------- >>> from sklearn.datasets import load_breast_cancer >>> from sklearn.linear_model import RidgeClassifier >>> X, y = load_breast_cancer(return_X_y=True) >>> clf = RidgeClassifier().fit(X, y) >>> clf.score(X, y) 0.9595...
Predict confidence scores for samples.
The confidence score for a sample is the signed distance of that sample to the hyperplane.
Parameters ---------- X : array_like or sparse matrix, shape (n_samples, n_features) Samples.
Returns ------- array, shape=(n_samples,) if n_classes == 2 else (n_samples, n_classes) Confidence scores per (sample, class) combination. In the binary case, confidence score for self.classes_1 where >0 means this class would be predicted.
Fit Ridge classifier model.
Parameters ---------- X : ndarray, sparse matrix of shape (n_samples, n_features) Training data.
y : ndarray of shape (n_samples,) Target values.
sample_weight : float or ndarray of shape (n_samples,), default=None Individual weights for each sample. If given a float, every sample will have the same weight.
.. versionadded:: 0.17 *sample_weight* support to Classifier.
Returns ------- self : object Instance of the estimator.
Get parameters for this estimator.
Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns ------- params : mapping of string to any Parameter names mapped to their values.
Predict class labels for samples in X.
Parameters ---------- X : array_like or sparse matrix, shape (n_samples, n_features) Samples.
Returns ------- C : array, shape n_samples Predicted class label per sample.
Return the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
Parameters ---------- X : array-like of shape (n_samples, n_features) Test samples.
y : array-like of shape (n_samples,) or (n_samples, n_outputs) True labels for X.
sample_weight : array-like of shape (n_samples,), default=None Sample weights.
Returns ------- score : float Mean accuracy of self.predict(X) wrt. y.
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object.
Parameters ---------- **params : dict Estimator parameters.
Returns ------- self : object Estimator instance.
Pretty-print the object to a formatter.