Bias vs Variance Explained in 50 Seconds

Описание: In this short video, we break down the bias-variance tradeoff in machine learning using two simple examples: Bobby’s overly simple line (high bias) and Timmy’s overly complex wiggly line (high variance).
You’ll learn how bias causes underfitting, variance causes overfitting, and why the key to good model performance is balancing the two.
Error = Bias² + Variance + Irreducible Noise — the sweet spot is where the model generalizes best.
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