mlquantify.metrics

AE	Compute the absolute error for each class or a dictionary of errors if input is a dictionary.
SE	Compute the mean squared error between the real and predicted prevalences.
MAE	Compute the mean absolute error between the real and predicted prevalences.
MSE	Mean Squared Error
KLD	Compute the Kullback-Leibler divergence between the real and predicted prevalences.
RAE	Compute the relative absolute error between the real and predicted prevalences.
NAE	Compute the normalized absolute error between the real and predicted prevalences.
NRAE	Compute the normalized relative absolute error between the real and predicted prevalences.
NKLD	Compute the normalized Kullback-Leibler divergence between the real and predicted prevalences.
NMD	Compute the Normalized Match Distance (NMD), also known as Earth Mover’s Distance (EMD), for ordinal quantification evaluation.
RNOD	Compute the Root Normalised Order-aware Divergence (RNOD) for ordinal quantification evaluation.
VSE	Compute the Variance-normalised Squared Error (VSE).
CvM_L1	Compute the L1 version of the Cramér–von Mises statistic (Xiao et al., 2006) between two cumulative distributions, as suggested by Bella et al. (2014).