package sklearn
Module Linear_model.RidgeClassifierCVSource
Source
val create :
?alphas:Arr.t ->
?fit_intercept:bool ->
?normalize:bool ->
?scoring:[ `S of string | `Callable of Py.Object.t ] ->
?cv:[ `I of int | `CrossValGenerator of Py.Object.t | `Arr of Arr.t ] ->
?class_weight:[ `DictIntToFloat of (int * float) list | `Balanced ] ->
?store_cv_values:bool ->
unit ->
tRidge classifier with built-in cross-validation.
See glossary entry for :term:`cross-validation estimator`.
By default, it performs Generalized Cross-Validation, which is a form of efficient Leave-One-Out cross-validation. Currently, only the n_features > n_samples case is handled efficiently.
Read more in the :ref:`User Guide <ridge_regression>`.
Parameters ---------- alphas : ndarray of shape (n_alphas,), default=(0.1, 1.0, 10.0) Array of alpha values to try. 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 (i.e. data is expected to be 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``.
scoring : string, callable, default=None A string (see model evaluation documentation) or a scorer callable object / function with signature ``scorer(estimator, X, y)``.
cv : int, cross-validation generator or an iterable, default=None Determines the cross-validation splitting strategy. Possible inputs for cv are:
- None, to use the efficient Leave-One-Out cross-validation
- integer, to specify the number of folds.
- :term:`CV splitter`,
- An iterable yielding (train, test) splits as arrays of indices.
Refer :ref:`User Guide <cross_validation>` for the various cross-validation strategies that can be used here.
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))``
store_cv_values : bool, default=False Flag indicating if the cross-validation values corresponding to each alpha should be stored in the ``cv_values_`` attribute (see below). This flag is only compatible with ``cv=None`` (i.e. using Generalized Cross-Validation).
Attributes ---------- cv_values_ : ndarray of shape (n_samples, n_targets, n_alphas), optional Cross-validation values for each alpha (if ``store_cv_values=True`` and ``cv=None``). After ``fit()`` has been called, this attribute will contain the mean squared errors (by default) or the values of the ``loss,score_func`` function (if provided in the constructor). This attribute exists only when ``store_cv_values`` is True.
coef_ : ndarray of shape (1, n_features) or (n_targets, 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``.
alpha_ : float Estimated regularization parameter
classes_ : ndarray of shape (n_classes,) The classes labels.
Examples -------- >>> from sklearn.datasets import load_breast_cancer >>> from sklearn.linear_model import RidgeClassifierCV >>> X, y = load_breast_cancer(return_X_y=True) >>> clf = RidgeClassifierCV(alphas=1e-3, 1e-2, 1e-1, 1).fit(X, y) >>> clf.score(X, y) 0.9630...
See also -------- Ridge : Ridge regression RidgeClassifier : Ridge classifier RidgeCV : Ridge regression 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.
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 with cv.
Parameters ---------- X : ndarray of shape (n_samples, n_features) Training vectors, where n_samples is the number of samples and n_features is the number of features. When using GCV, will be cast to float64 if necessary.
y : ndarray of shape (n_samples,) Target values. Will be cast to X's dtype if necessary.
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.
Returns ------- self : object
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.