Answer / Krishn Mohan Pandey

The reason Manhattan distance is not typically used in the k-nearest neighbors (knn) algorithm is because it considers only the absolute differences along each feature dimension. This can lead to a less accurate calculation of similarity for data that exhibits skewness or non-linear patterns, as it ignores the direction of change in the features. Euclidean distance, which is more commonly used in knn, takes into account both the magnitude and direction of differences between points, resulting in a more suitable measure of similarity.

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