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

Author(s): Ajit

Originally published on Towards AI.

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

Classification metrics are the cheat codes to figure out if your machine learning model is actually doing a good job or just pretending to. Whether you’re building a spam detector, a health diagnosis tool, or trying to make sense of what people are feeling online, knowing how to measure your model’s performance is key. In this article, I’m sharing some must-know questions and answers that often pop up in data science interviews.

The article discusses various classification metrics essential for evaluating machine learning models in the context of data science interviews. It covers important topics such as confusion matrices, accuracy, precision, recall, F-scores, ROC curves, and more, providing a comprehensive understanding of how to assess model performance effectively. Each section includes common questions and detailed answers, offering insights into practical scenarios, the trade-offs between different metrics, and best practices for model evaluation.

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Published via Towards AI