scikits.learn.linear_model.Ridge

class scikits.learn.linear_model.Ridge(alpha=1.0, fit_intercept=True)

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).

Examples

>>> from scikits.learn.linear_model import Ridge
>>> import numpy as np
>>> n_samples, n_features = 10, 5
>>> np.random.seed(0)
>>> y = np.random.randn(n_samples)
>>> X = np.random.randn(n_samples, n_features)
>>> clf = Ridge(alpha=1.0)
>>> clf.fit(X, y)
Ridge(alpha=1.0, fit_intercept=True)

Methods

__init__(alpha=1.0, fit_intercept=True)

fit(X, y, sample_weight=1.0, solver='default', **params)

Fit Ridge regression model

Parameters :

X : numpy array of shape [n_samples,n_features]

Training data

y : numpy array of shape [n_samples]

Target values

sample_weight : float or numpy array of shape [n_samples]

Sample weight

solver : ‘default’ | ‘cg’

Solver to use in the computational routines. ‘default’ will use the standard scipy.linalg.solve function, ‘cg’ will use the a conjugate gradient solver as found in scipy.sparse.linalg.cg.

Returns :

self : returns an instance of self.

predict(X)

Predict using the linear model

Parameters :

X : numpy array of shape [n_samples, n_features]

Returns :

C : array, shape = [n_samples]

Returns predicted values.

score(X, y)

Returns the coefficient of determination of the prediction

Parameters :

X : array-like, shape = [n_samples, n_features]

Training set.

y : array-like, shape = [n_samples]

Returns :

z : float