mc
short answer
essay
Prompt
[CHOICE] Minimum Distance classifier — how it works + pros/cons.
Plain English answer (default view) — what you'd actually write on the test
Find the **average** color of each known class, then assign each pixel to whichever class average it's **closest** to. That's the whole rule. **Pros:** - Fast. - Never leaves a pixel unclassified. **Cons:** - Ignores class **shape** (a tight class and a loose class are treated identically). - Force-fits weird pixels that should probably be flagged as *unclassified*. Good for a quick first pass. Use Max Likelihood if you want accuracy.
🔬 Technical version / model bullets (revealed on click) — one bullet per line
For each pixel, compute Euclidean distance to every class mean and assign to the closest. Strength: fast, works with small training sets. Weakness: ignores class shape/variance → poor on classes with different spreads or correlations. Everything gets classified (no unclassified pixels) — can force-fit outliers.
💡 Mnemonic / memory aid (shown on hover)
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