3.2.4.1.10. sklearn.linear_model.RidgeClassifierCV
-
class sklearn.linear_model.RidgeClassifierCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, normalize=False, scoring=None, cv=None, class_weight=None, store_cv_values=False)
[source]
-
Ridge classifier with built-in cross-validation.
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 User Guide.
Parameters: |
-
alphas : numpy array of shape [n_alphas] -
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 : boolean -
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 : boolean, optional, 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 sklearn.preprocessing.StandardScaler before calling fit on an estimator with normalize=False . -
scoring : string, callable or None, optional, 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, optional -
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.
- An object to be used as a cross-validation generator.
- An iterable yielding train/test splits.
Refer User Guide for the various cross-validation strategies that can be used here. -
class_weight : dict or ‘balanced’, optional -
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 : boolean, 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_ : array, 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). -
coef_ : array, shape = [n_features] or [n_targets, n_features] -
Weight vector(s). -
intercept_ : float | array, shape = (n_targets,) -
Independent term in decision function. Set to 0.0 if fit_intercept = False . -
alpha_ : float -
Estimated regularization parameter |
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 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...
Methods
decision_function (X) | Predict confidence scores for samples. |
fit (X, y[, sample_weight]) | Fit the ridge classifier. |
get_params ([deep]) | Get parameters for this estimator. |
predict (X) | Predict class labels for samples in X. |
score (X, y[, sample_weight]) | Returns the mean accuracy on the given test data and labels. |
set_params (**params) | Set the parameters of this estimator. |
-
__init__(alphas=(0.1, 1.0, 10.0), fit_intercept=True, normalize=False, scoring=None, cv=None, class_weight=None, store_cv_values=False)
[source]
-
decision_function(X)
[source]
-
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(X, y, sample_weight=None)
[source]
-
Fit the ridge classifier.
Parameters: |
-
X : array-like, shape (n_samples, n_features) -
Training vectors, where n_samples is the number of samples and n_features is the number of features. -
y : array-like, shape (n_samples,) -
Target values. Will be cast to X’s dtype if necessary -
sample_weight : float or numpy array of shape (n_samples,) -
Sample weight. |
Returns: |
-
self : object |
-
get_params(deep=True)
[source]
-
Get parameters for this estimator.
Parameters: |
-
deep : boolean, optional -
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(X)
[source]
-
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. |
-
score(X, y, sample_weight=None)
[source]
-
Returns 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, shape = (n_samples, n_features) -
Test samples. -
y : array-like, shape = (n_samples) or (n_samples, n_outputs) -
True labels for X. -
sample_weight : array-like, shape = [n_samples], optional -
Sample weights. |
Returns: |
-
score : float -
Mean accuracy of self.predict(X) wrt. y. |
-
set_params(**params)
[source]
-
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.