Author(s): Shahidullah Kawsar

Originally published on Towards AI.

Machine Learning Interview Preparation Part 03 Solution

Regularization in machine learning means putting limits on a model so it does not become too complicated. It is like guardrails on a road that help keep a car on track. These limits stop the model from memorizing the training data and instead help it learn useful patterns. As a result, the model works better on new data it has never seen before and is more reliable in real-world use.

This article provides a set of 20 regularization-related interview questions and answers aimed at helping candidates prepare for machine learning interviews. It explains key concepts related to Ridge and Lasso regression, including their definitions, advantages, and correct interpretations of their parameters. Each question is followed by a concise explanation of the correct answer, elucidating how regularization techniques can help mitigate overfitting and improve model performance.

Read the full blog for free on Medium.

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Published via Towards AI