Ridge regression addresses some of the problems of
ordinary_least_squares by imposing a penalty on the size of the
coefficients. The ridge coefficients minimize a penalized residual sum
of squares:
The complexity paramete controls the amount of shrinkage the larger the value of , the greater the amount of shrinkage and thus the coefficients become more robust to collinearity.
As with other linear models, Ridge will take in its fit method
arrays X, y and will store the coefficients w of the linear model in
its coef_ member:
>>> from sklearn import linear_model
>>> reg = linear_model.Ridge(alpha=.5)
>>> reg.fit([[0, 0], [0, 0], [1, 1]], [0, .1, 1])
Ridge(alpha=0.5)
>>> reg.coef_
array([0.34545455, 0.34545455])
>>> reg.intercept_
0.13636...