Discuss the difference between Euclidean distance and Manhattan distance metrics in the context of KNN.
- Euclidean is faster, Manhattan is more accurate
- Euclidean is for 3D, Manhattan for 2D
- Euclidean is for continuous data, Manhattan for categorical
- Euclidean uses squares, Manhattan uses absolutes
Euclidean distance is the square root of the sum of squared differences, while Manhattan distance is the sum of the absolute differences.
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