What is kernel density estimation?
mediumAnswer
- Non-parametric density estimate: KDE(x) = (1/(nh)) Σ , where K is a kernel (usually Gaussian) and h is bandwidth.
- Bandwidth choice is the key knob: too small → wiggly, too large → over-smoothed.
- Silverman's rule: h ≈ 1.06 * σ * n^(-1/5).
- Better: cross-validation.
- Smoother alternative to histograms for continuous data; also foundation of some non-parametric classifiers (KDE-NB, Parzen windows).
Check yourself — multiple choice
- Parametric only
- Sum of scaled kernels around each data point; bandwidth h controls smoothness; Silverman / CV to pick h
- Same as histogram
- Requires normality
KDE: sum of kernels; bandwidth is the key hyperparameter (Silverman or CV).
#descriptive#eda#estimation
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