ordinal-classification

问题 I'm using Keras to try to predict a vector of scores (0-1) using a sequence of events. For example, X is a sequence of 3 vectors comprised of 6 features each, while y is a vector of 3 scores: X [ [1,2,3,4,5,6], <--- dummy data [1,2,3,4,5,6], [1,2,3,4,5,6] ] y [0.34 ,0.12 ,0.46] <--- dummy data I want to adress the problem as ordinal classification, so if the actual values are [0.5,0.5,0.5] the prediction [0.49,0.49,0.49] is better then [0.3,0.3,0.3] . My Original solution, was to use sigmoid

问题 I'm using Keras to try to predict a vector of scores (0-1) using a sequence of events. For example, X is a sequence of 3 vectors comprised of 6 features each, while y is a vector of 3 scores: X [ [1,2,3,4,5,6], <--- dummy data [1,2,3,4,5,6], [1,2,3,4,5,6] ] y [0.34 ,0.12 ,0.46] <--- dummy data I want to adress the problem as ordinal classification, so if the actual values are [0.5,0.5,0.5] the prediction [0.49,0.49,0.49] is better then [0.3,0.3,0.3] . My Original solution, was to use sigmoid