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Machine learning interview questions on Regularization

Hi Everyone I am going to start posting Interview questions topicwise. This is part 2 of the posts where I’ll list down the interview questions based on Regularization and don’t forget to share it as much as you can to make it accessible to everyone and let others know about it.

What is regularization and why is it used in machine learning?

Regularization is a technique in machine learning used to prevent overfitting by adding a penalty to the model’s objective function. It encourages, simpler models and better generalization. Regularization can be achieved through methods like L1 and L2 regularization.

What is the difference between L1 and L2 ?

L1 Regularization (Lasso Regularization):
Penalty term is proportional to the absolute value of the coefficients.
Encourages sparsity by driving some coefficients to exactly zero, effectively performing feature selection.
Suitable for situations where some features are less relevant, leading to a more interpretable model.

L2 Regularization (Ridge Regularization):
Penalty term is proportional to the square of the coefficients.
Shrinks the coefficients towards zero but rarely makes them exactly zero.
Helps to mitigate the impact of multicollinearity and is generally more stable in the presence of highly correlated features.

Explain the concept of ridge regression and its role in regularization.

Ridge regression, also known as L2 regularization, is a linear regression technique that addresses the problem of multicollinearity (high correlation between predictor variables) and overfitting in a regression model. It does so by adding a penalty term to the linear regression cost function, encouraging the model to keep the coefficient values of the predictor variables small. This helps to stabilize the model and reduces the sensitivity to small changes in the input data. In traditional linear regression, the goal is to find the best-fitting line (or hyperplane in higher dimensions) that minimizes the sum of squared errors between the predicted values and the actual target values. In ridge regression, the cost function is augmented with a regularization term, which is proportional to the sum of the squares of the coefficients.

What is the elastic net regularization and how does it combine L1 and L2 penalties?

Elastic Net regularization is a variant of linear regression that combines both L1 (Lasso) and L2 (Ridge) penalties to address the limitations of each individual regularization technique. By including both L1 and L2 penalties, Elastic Net takes advantage of the strengths of Lasso (sparse feature selection) and Ridge (stability) regularization methods while mitigating their weaknesses. 

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