Ridge Regression

Preliminaries

# Load libraries
from sklearn.linear_model import Ridge
from sklearn.datasets import load_boston
from sklearn.preprocessing import StandardScaler

Load Boston Housing Dataset

# Load data
boston = load_boston()
X = boston.data
y = boston.target

Standardize Features

# Standarize features
scaler = StandardScaler()
X_std = scaler.fit_transform(X)

Fit Ridge Regression

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