What is multicollinearity in machine learning?

What is multicollinearity in machine learning?

Multicollinearity refers to a situation in machine learning where two or more predictor variables are highly correlated, making it difficult to determine their individual effects on the target variable.

Why is multicollinearity a problem?

It can lead to unreliable coefficient estimates, inflated standard errors, and ultimately affect the model's performance and interpretability.

How can I detect multicollinearity?

You can detect multicollinearity using Variance Inflation Factor (VIF), correlation matrices, or by examining the condition number of the design matrix.

What are some ways to handle multicollinearity?

You can handle it by removing highly correlated predictors, combining them, or using techniques like Ridge regression that can mitigate its effects.