Learn about micro and macro averages in multiclass multilabel problems, the difference between multiclass and multilabel problems and when to use micro and macro averages.

Learn about metrics used to compare histograms with examples of how to calculate them in python. From Chi-Squared distance to Kullback-Leibler divergence and Earth Mover's distance. A comprehensive guide.

"This post describe evaluation metrics used in Kaggle competitions where problem to solve is has regression nature. Eight different metrics are described, namely - Absolute Error (AE), Mean Absolute Error (MAE), Weighted Mean Absolute Error (WMAE), Pearson Correlation Coefficient, Spearman\u2019s Rank Correlation, Root Mean Squared Error (RMSE), Root Mean Squared Logarithmic Error (RMSLE), Mean Columnwise Root Mean Squared Error (MCRMSE)."