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F1 Score: Your Key Metric for Imbalanced Data — But Do You Really Know Why?
Welcome back to our journey with the ‘Courage to Learn ML’ series. In this session, we’re exploring the nuanced world of metrics. Many resources introduce these metrics or delve into their mathematical aspects, yet the logic behind these ‘simple’ maths can sometimes remain opaque. For those new to this topic, I recommend checking out Shervin’s thorough post along with the comprehensive guide from neptune.ai.
In typical data science interview preparations, when addressing how to handle imbalanced data, the go-to metric is often the F1 score, known as the harmonic mean of recall and precision. However, the rationale behind why the F1 score is particularly suitable for such cases is frequently left unexplained. This post is dedicated to unraveling these reasons, helping you understand the choice of specific metrics in various scenarios.
As usual, this post will outline all the questions we’re tackling. If you’ve been pondering these same queries, you’re in the right place:
- What exactly are precision and recall, and how can we intuitively understand them?
- Why are precision and recall important, and why do they often seem to conflict with each other? Is it possible to achieve high levels of both?
- What’s the F1 score, and why do we calculate it as the harmonic mean of recall and precision?
- Why is the F1 score frequently used for imbalanced data? Is it only useful in these scenarios?
- How does the interpretation of the F1 score change when the positive class is the majority?
- What’s the difference between PR and ROC curves, and when should we prefer using one over the other?
With a fundamental understanding of these metrics, our learner approaches the mentor, who is busy doing laundry, with the first question:
I’m working on a game recommendation system. It’s designed to suggest video games based on users’ preferences and lifestyles. But I’ve…
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