v Ridge Regression - Machine Learning

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)