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 thatβs too simple and one thatβs too complex. This is known as the Bias-Variance Tradeoff, and itβs the key to building models that can make accurate predictions without overfitting or underfitting.
In this blog, weβll 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, weβll also sprinkle in code snippets to visualize these concepts.
Data Scientist finding balance in bias and varianceImagine youβre learning how to shoot arrows at a target. The goal is to hit the bullβs-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 βarrowsβ (predictions) to hit close to the bullβs-eye as often as possible, with minimal spread.
Bias refers to how far off your modelβs predictions are from the actual values β like if you… Read the full blog for free on Medium.
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Published via Towards AI