sklearn.linear_model
.RidgeClassifier¶
- class sklearn.linear_model.RidgeClassifier(alpha=1.0, *, fit_intercept=True, normalize='deprecated', copy_X=True, max_iter=None, tol=0.001, class_weight=None, solver='auto', random_state=None)[source]¶
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).Read more in the User Guide.
- Parameters
- alphafloat, 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
1 / (2C)
in other linear models such asLogisticRegression
orLinearSVC
.- fit_interceptbool, 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).
- normalizebool, 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 useStandardScaler
before callingfit
on an estimator withnormalize=False
.Deprecated since version 1.0:
normalize
was deprecated in version 1.0 and will be removed in 1.2.- copy_Xbool, default=True
If True, X will be copied; else, it may be overwritten.
- max_iterint, default=None
Maximum number of iterations for conjugate gradient solver. The default value is determined by scipy.sparse.linalg.
- tolfloat, default=1e-3
Precision of the solution.
- class_weightdict 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
andmax_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.
New in version 0.17: Stochastic Average Gradient descent solver.
New in version 0.19: SAGA solver.
- random_stateint, RandomState instance, default=None
Used when
solver
== ‘sag’ or ‘saga’ to shuffle the data. See Glossary for details.
- 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.
- n_features_in_int
Number of features seen during fit.
New in version 0.24.
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...
Methods
Predict confidence scores for samples.
fit
(X, y[, sample_weight])Fit Ridge classifier model.
get_params
([deep])Get parameters for this estimator.
predict
(X)Predict class labels for samples in X.
score
(X, y[, sample_weight])Return the mean accuracy on the given test data and labels.
set_params
(**params)Set the parameters of this estimator.
- decision_function(X)[source]¶
Predict confidence scores for samples.
The confidence score for a sample is proportional to the signed distance of that sample to the hyperplane.
- Parameters
- Xarray-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 Ridge classifier model.
- Parameters
- X{ndarray, sparse matrix} of shape (n_samples, n_features)
Training data.
- yndarray of shape (n_samples,)
Target values.
- sample_weightfloat or ndarray of shape (n_samples,), default=None
Individual weights for each sample. If given a float, every sample will have the same weight.
New in version 0.17: sample_weight support to Classifier.
- Returns
- selfobject
Instance of the estimator.
- get_params(deep=True)[source]¶
Get parameters for this estimator.
- Parameters
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
- paramsdict
Parameter names mapped to their values.
- predict(X)[source]¶
Predict class labels for samples in X.
- Parameters
- Xarray-like or sparse matrix, shape (n_samples, n_features)
Samples.
- Returns
- Carray, shape [n_samples]
Predicted class label per sample.
- score(X, y, sample_weight=None)[source]¶
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
- Xarray-like of shape (n_samples, n_features)
Test samples.
- yarray-like of shape (n_samples,) or (n_samples, n_outputs)
True labels for
X
.- sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
- Returns
- scorefloat
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
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
- **paramsdict
Estimator parameters.
- Returns
- selfestimator instance
Estimator instance.