The Bias-Variance Tradeoff
Explore the bias variance tradeoff in machine learning to understand how model complexity affects prediction accuracy. This lesson uses a dart throwing analogy to clarify high and low bias and variance scenarios, helping you identify underfitting and overfitting. You will gain insight into crafting models that balance bias and variance to improve performance on unseen data.
A critical concept in machine learning
The bias-variance tradeoff is widely considered a critical concept in machine learning. The most valuable machine learning models can produce accurate predictions on new, unseen data. As a machine learning practitioner, crafting valuable models requires carefully balancing the bias-variance tradeoff.
This lesson builds on the knowledge of underfitting and overfitting to understand the bias-variance tradeoff. Tuning models for the bias-variance tradeoff using the cross-validation technique will be covered later in the course.
The dart throwing analogy
An intuitive way to learn the bias-variance tradeoff is by using the analogy of throwing darts at a dartboard. To make this analogy relatable, consider a hypothetical dart enthusiast named Bob.
Bob is an avid participant in dart throwing competitions and has decided to improve his skill. To allow himself ample time to practice, Bob purchases a dartboard at home.