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Supervised vs. unsupervised classification — key differences?
Plain English (default view) — short, conversational, lightly seasoned with science
🔬 Scientific / formula (revealed on click) — markdown + $$…$$ ok
Two camps of pixel classification: - 👨🏫 **Supervised** — needs a teacher - Algorithm: **Maximum Likelihood** (most common) - A priori knowledge: **required** — you give it training samples - Control: **user-driven** — you set the class scheme - Strength: more accurate when training is good - 🤖 **Unsupervised** — clusters on its own - Algorithm: **ISODATA** (or K-means) - A priori knowledge: **not required** — no training - Control: **computer-automated** — it finds the groupings - Strength: reveals natural groupings, fast first pass After unsupervised you still **label the clusters** by hand — that's where Recode comes in.
💡 Mnemonic / memory aid (shown on hover)
Supervised needs a TEACHER (training samples). Unsupervised is CLUSTERING — the computer invents the classes, you label afterward.
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