mc
short answer
essay
Prompt
[CHOICE] Maximum Likelihood classifier — how it works and its main assumption.
Plain English answer (default view) — what you'd actually write on the test
Picture each class as a **fuzzy cloud floating in color-space**. Max Likelihood asks: *"Which cloud is this pixel most likely from?"* Crucially, it accounts for each cloud's **shape** — not just its center. A tight class is treated differently from a sprawling one. **The catch:** it assumes the clouds are roughly **Gaussian blobs**. If your classes are weird-shaped (banana-shaped, donut-shaped), ML's accuracy drops. It's the most popular supervised classifier — but only when the data fits the assumption.
🔬 Technical version / model bullets (revealed on click) — one bullet per line
Assumes each class's pixels follow a **multivariate Gaussian** distribution. For each pixel, compute probability of membership in every class (from class mean + covariance). Assign pixel to the class with the **highest probability**. Strength: statistically principled, handles correlated bands. Weakness: needs enough training samples to estimate covariance; struggles if classes aren't Gaussian.
💡 Mnemonic / memory aid (shown on hover)
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