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. Let me know if you’d like to dive deeper into any part of this!