Evaluation Metrics For Regression: Must-Know Questions and Answers for Data Science Interviews

Author(s): Ajit

Originally published on Towards AI.

Evaluation Metrics For Regression: Must-Know Questions and Answers for Data Science Interviews

Hey everyone! 👋 I’ve put together a concise and practical guide on regression evaluation metrics, complete with interview questions and answers, many inspired by real-world data science interviews. Whether you’re gearing up for a machine learning or data science role, or just aiming to deepen your understanding of model evaluation, this resource is designed to help you grasp the essentials and build confidence.

This article provides a comprehensive overview of essential regression evaluation metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and more. Each section tackles common interview questions like definitions, advantages, and disadvantages. It covers practical implementations and provides Python functions for calculating these metrics, while also addressing the context in which different metrics should be applied to ensure robustness in data sciences.

Read the full blog for free on Medium.

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Published via Towards AI