bias-variance-tradeoff #

Shows f(x) (true function) and multiple learned f_hat(x) curves from different training sets. At a fixed input x, it visualizes bias as the gap between f(x) and E[f_hat(x)], variance as the spread of f_hat(x) across datasets, and a stacked bar for noise + bias^2 + variance. Model complexity animates over time to demonstrate underfitting (high bias) to overfitting (high variance).

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⏮◀◀▶▶STEP0.25x1xZOOM

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practical uses #

01.Choosing model capacity/regularization strength (e.g., tree depth, polynomial degree, neural net size)
02.Explaining why cross-validation helps estimate generalization error
03.Diagnosing underfitting vs overfitting and selecting bias/variance reduction strategies

technical notes #

Deterministically simulates multiple training sets with noisy samples around a fixed true function. A complexity parameter blends a low-variance linear fit with a high-variance kernel smoother; predictions at a fixed x are aggregated to estimate bias and variance. Uses snapped drawing, green-on-black palette, and a 4.2s ping-pong animation cycle.

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