When a machine learning model makes a prediction, often times we would like to determine which features of the input (pixels, for images) were important for the prediction. If the model makes a misprediction, we might want to know which features contributed to the misclassification. We can visualize the feature importance mask as a grayscale image with the same dimensions as the original image with brightness corresponding to importance of the pixel.