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Four common decision rules for supervised classification?
Plain English (default view) — short, conversational, lightly seasoned with science
🔬 Scientific / formula (revealed on click) — markdown + $$…$$ ok
The four ways the computer assigns a pixel to a class, ranked by sophistication: - 📦 **Parallelepiped** - How: box-shaped regions defined by each class's min/max in every band - Pro: simple, fast first pass - Con: leaves **gaps** + has **corner overlap** problems - 📏 **Minimum Distance** - How: assign to whichever class **mean** is closest (Euclidean) - Pro: no unclassified pixels, very fast - Con: ignores class **shape**, force-fits outliers - 📐 **Mahalanobis Distance** - How: Min Distance scaled by each class's **covariance** - Pro: accounts for class shape (ellipsoidal) - Con: still assumes equal priors - 🎯 **Maximum Likelihood** - How: pick the most probable class, assuming Gaussian distributions - Pro: **most accurate** when classes are well-sampled - Con: needs lots of training data, fails on non-Gaussian classes
💡 Mnemonic / memory aid (shown on hover)
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