The concepts of overfitting and underfitting are pretty well understood by most folks.

Yet, here’s another neat way to understand them intuitively.

Imagine you want to estimate a probability density function (PDF) using a histogram.

Your estimation entirely depends on the bin width:

Creating small bins will overfit the PDF. This leads to high variance.

Creating large bins will underfit the PDF. This leads to high bias.

This is depicted in the image above.

Overall, the whole bias-variance problem is about finding the optimal bin width.

I first read this analogy in the book “**All of Statistics**” a couple of years back and found it to be pretty intuitive and neat.

Here’s the book if anyone’s interested in learning more: **All of Statistics PDF**. **Page 306 inspired today’s post.**

Hope that helped :)

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## A Visual and Intuitive Guide to The Bias-Variance Problem

The disadvantage of overfitting would be better illustrated if the "measured signal/data" incorporated a few "dents" and "bumps" (of the kind one wouldn't want fitted).