6.2.2. 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 coditioning of the problem and reduce the variance of the estimates.
fit_intercept : boolean
wether 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
fit(X, y, **params) Fit Ridge regression model predict(X) Predict using the linear model score(X, y) Returns the coefficient of determination of the prediction - __init__(alpha=1.0, fit_intercept=True)¶
- fit(X, y, **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
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