The Balancing Act of Machine Learning: Bias-Variance Tradeoff

Author(s): Souradip Pal

Originally published on Towards AI.

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Once upon a time, in the world of machine learning, data scientists faced a constant challenge: finding the balance between a model that’s too simple and one that’s too complex. This is known as the Bias-Variance Tradeoff, and it’s the key to building models that can make accurate predictions without overfitting or underfitting.

In this blog, we’ll break down bias, variance, what happens when you underfit or overfit, and how techniques like bagging, boosting, and regularization can help us strike that perfect balance. To make things more engaging, we’ll also sprinkle in code snippets to visualize these concepts.

Data Scientist finding balance in bias and variance

Imagine you’re learning how to shoot arrows at a target. The goal is to hit the bull’s-eye. Sometimes, your arrows consistently miss the mark in the same way (this is bias). Other times, they scatter all over the place (this is variance). The same thing happens when training machine learning models. You want your “arrows” (predictions) to hit close to the bull’s-eye as often as possible, with minimal spread.

Bias refers to how far off your model’s predictions are from the actual values — like if you… Read the full blog for free on Medium.

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Published via Towards AI