I have a few models doing prediction with 4 classes, with the output precision and recall varying with different labels.
For example I have (with the class labels being 0, 1, 2, 3 on the x axis):
I understand from a previous question (Recall and precision in classification) that the differences a model can have at predicting different labels can be related to the cost for different mistakes, but I would like to understand in the context of multilabel classification - why is it the models differ for different labels in precision and recall? Are they just showing that they are better at recognising specific patterns that relate to specific labels? Or is there some way I can investigate further as to why this is happening?
Also, generally speaking, would consistency in precision and recall across labels (for example in my graph it seems the DeepSuperLearner is the most consistent/nearest to a straight line for both) make a model more worthwhile than a model which is for example, really good at labels 1 and 3 but bad/medicore at predicting labels 0 and 2?