F1 Score Unveiled: Understanding the Key Metric in Machine Learning!
The other day, I was in a team meeting where we talked about different types of models and how we use them.
One of our Engineering Leaders asked about something called as F1 score for these models, and none of us knew what it was! It made me wonder about all the fancy words people use in meetings these days.
It seems like there’s always some new jargon flying around. In this series of articles, we’ll go over a few of these buzzwords and try to understand what they really mean.
F1 score:
Back in your undergrad days, you probably studied different types of averages like the Arithmetic Mean (AM), Geometric Mean (GM), and Harmonic Mean (HM). But back then, the main focus was on understanding how to use these averages to solve problems on exams. No one really explained how these averages could be useful in everyday life situations.
F1 score is the “harmonic mean” between Precision and Recall, and is calculated as 2/((1/Precision)+(1/Recall)).
As you know the Harmonic mean of n numbers are calculated as n / [1/x1 + 1/x2 + 1/x3 + … + 1/xn].
Once I understood this, I thought, “Wow, is it really that easy?” :)
Let’s discuss why we use the Harmonic Mean here. In machine learning, there are two important measures to understand how well different algorithms or models perform. The first one is called Precision, and the second one is called Recall. You have to adjust these two measures to improve your model’s performance.
It’s really tricky to get both of them to be perfect at the same time. So, if you’re trying to rank how well different algorithms perform, which measure should you rely on? That’s where calculating the F1 score comes into play. Using this single score, you can quickly figure out which models are performing the best.
But the question stays the same: Why use the Harmonic Mean here? The Harmonic Mean is usually used to balance the importance of different values when calculating a mean. It treats each data point equally.
In the next article, I’ll discuss Precision and Recall in machine learning models.
Happy learning! If you’d like a one-on-one session, you can schedule a slot with me at: https://topmate.io/adityaroshan
