How would you deal with multicollinearity using Scikit-learn when training a Linear Regression model

Question

Can you tell me How would you deal with multicollinearity using Scikit-learn when training a Linear Regression model?

Answer 1

You can address multicollinearity in Linear Regression by using Ridge regression in Scikit-learn, which applies L2 regularization to reduce feature coefficient sensitivity.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• Ridge(alpha=1.0) applies L2 regularization, shrinking coefficients to mitigate multicollinearity.
• make_regression() with effective_rank=5 simulates multicollinear features.
• mean_squared_error() evaluates the model’s predictive accuracy.
• alpha controls the regularization strength — higher values increase penalty on large coefficients.

Hence, Ridge regression effectively reduces the impact of multicollinearity by stabilizing coefficient estimates, improving model generalization and performance.