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In this video tutorial, Shrini discusses the best way to evaluate semantic segmentation using the intersection over union approach. Semantic segmentation involves classifying individual pixels rather than whole images, such as distinguishing between different minerals in a rock sample. This approach is more effective than using accuracy metrics, as accuracy can be misleading in cases of class imbalance in multi-class problems. The focus is on accurately classifying individual pixels, making intersection over union a more suitable evaluation method.