The skin detection classifier performs a skin detection task using a Bayesian approach. It starts by reading and resizing images and masks for alignment, with the masks used to differentiate skin and non-skin pixels in the reference image. Pixel histograms for each color channel are generated to estimate the probability distributions of skin and non-skin pixels. These distributions are then used to calculate the likelihood of each pixel in a test image being skin or not. A decision is made for each pixel based on the higher likelihood, generating a detected mask. Performance metrics such as true positive, true negative, false positive, and false negative rates are computed to evaluate detection accuracy against the ground truth mask. Finally, the test image, ground truth mask, and detected mask are visualized side-by-side.