sions with this class for class on the other handprecision is because no other class was mistakenly classified as while for class there are no false negativesso the recall is we can also see that the model has particular difficulties with classes and the most commonly used metric for imbalanced datasets in the multiclass setting is the multiclass version of the -score the idea behind the multiclass -score is to compute one binary -score per classwith that class being the positive class and the other classes making up the negative classes thenthese per-class -scores are averaged using one of the following strategies"macroaveraging computes the unweighted per-class -scores this gives equal weight to all classesno matter what their size is "weightedaveraging computes the mean of the per-class -scoresweighted by their support this is what is reported in the classification report "microaveraging computes the total number of false positivesfalse negativesand true positives over all classesand then computes precisionrecalland fscore using these counts if you care about each sample equally muchit is recommended to use the "microaverage -scoreif you care about each class equally muchit is recommended to use the "macroaverage -scorein[ ]print("micro average score{ }format ( _score(y_testpredaverage="micro"))print("macro average score{ }format ( _score(y_testpredaverage="macro"))out[ ]micro average score macro average score regression metrics evaluation for regression can be done in similar detail as we did for classification-for exampleby analyzing overpredicting the target versus underpredicting the target howeverin most applications we've seenusing the default used in the score method of all regressors is enough sometimes business decisions are made on the basis of mean squared error or mean absolute errorwhich might give incentive to tune models using these metrics in generalthoughwe have found to be more intuitive metric to evaluate regression models evaluation metrics and scoring