sklearn.linear_model.RidgeClassifier¶
- class sklearn.linear_model.RidgeClassifier(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, class_weight=None, solver='auto')¶
Classifier using Ridge regression.
Parameters: alpha : float
Small positive values of alpha improve the conditioning of the problem and reduce the variance of the estimates. Alpha corresponds to (2*C)^-1 in other linear models such as LogisticRegression or LinearSVC.
class_weight : dict, optional
Weights associated with classes in the form {class_label : weight}. If not given, all classes are supposed to have weight one.
copy_X : boolean, optional, default True
If True, X will be copied; else, it may be overwritten.
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).
max_iter : int, optional
Maximum number of iterations for conjugate gradient solver. The default value is determined by scipy.sparse.linalg.
normalize : boolean, optional, default False
If True, the regressors X will be normalized before regression.
solver : {‘auto’, ‘svd’, ‘cholesky’, ‘lsqr’, ‘sparse_cg’}
Solver to use in the computational routines. ‘svd’ will use a Singular value decomposition to obtain the solution, ‘cholesky’ will use the standard scipy.linalg.solve function, ‘sparse_cg’ will use the conjugate gradient solver as found in scipy.sparse.linalg.cg while ‘auto’ will chose the most appropriate depending on the matrix X. ‘lsqr’ uses a direct regularized least-squares routine provided by scipy.
tol : float
Precision of the solution.
Attributes: `coef_` : array, shape = [n_features] or [n_classes, n_features]
Weight vector(s).
See also
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.
Methods
decision_function(X) Predict confidence scores for samples. fit(X, y) Fit Ridge regression model. 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__(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, max_iter=None, tol=0.001, class_weight=None, solver='auto')¶
- decision_function(X)¶
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, 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)¶
Fit Ridge regression model.
Parameters: X : {array-like, sparse matrix}, shape = [n_samples,n_features]
Training data
y : array-like, shape = [n_samples]
Target values
Returns: self : returns an instance of self.
- get_params(deep=True)¶
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)¶
Predict class labels for samples in X.
Parameters: X : {array-like, 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)¶
Returns the mean accuracy on the given test data and labels.
Parameters: X : array-like, shape = (n_samples, n_features)
Test samples.
y : array-like, shape = (n_samples,)
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)¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns: self :
