Ridge Regression

Preliminaries


from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler


X = boston.data
y = boston.target

Standardize Features


/* Standarize features */
scaler = StandardScaler()
X_std = scaler.fit_transform(X)

Fit Ridge Regression

The hyperparameter, α, lets us control how much we penalize the coefficients, with higher values of α creating simpler modelers. The ideal value of α should be tuned like any other hyperparameter. In scikit-learn, α is set using the alpha parameter.


/* Create ridge regression with an alpha value */
regr = Ridge(alpha=0.5)

/* Fit the linear regression */
model = regr.fit(X_std, y)

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