8.15.1.3. sklearn.linear_model.RidgeClassifier¶
- class sklearn.linear_model.RidgeClassifier(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, tol=0.001, class_weight=None)¶
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.
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
If True, the regressors X are normalized
copy_X : boolean, optional, default True
If True, X will be copied; else, it may be overwritten.
tol : float
Precision of the solution.
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.
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.
Attributes
coef_ array, shape = [n_features] or [n_classes, n_features] Weight vector(s). Methods
decision_function(X) Decision function of the linear model fit(X, y[, solver]) Fit Ridge regression model. get_params([deep]) Get parameters for the estimator predict(X) Predict target values according to the fitted model. score(X, y) Returns the mean accuracy on the given test data and labels. set_params(**params) Set the parameters of the estimator. - __init__(alpha=1.0, fit_intercept=True, normalize=False, copy_X=True, tol=0.001, class_weight=None)¶
- decision_function(X)¶
Decision function of the linear model
Parameters : X : numpy array of shape [n_samples, n_features]
Returns : C : array, shape = [n_samples]
Returns predicted values.
- fit(X, y, solver='auto')¶
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
solver : {‘auto’, ‘dense_cholesky’, ‘sparse_cg’}
Solver to use in the computational routines. ‘dense_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.
Returns : self : returns an instance of self.
- get_params(deep=True)¶
Get parameters for the estimator
Parameters : deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- predict(X)¶
Predict target values according to the fitted model.
Parameters : X : array-like, shape = [n_samples, n_features] Returns : y : array, shape = [n_samples]
- score(X, y)¶
Returns the mean accuracy on the given test data and labels.
Parameters : X : array-like, shape = [n_samples, n_features]
Training set.
y : array-like, shape = [n_samples]
Labels for X.
Returns : z : float
- set_params(**params)¶
Set the parameters of the 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 :