I have used a adabag(boosting + bagging) model on imbalanced dataset (6% positive), I have tried to maximized the sensitivity while keeping the accuracy above 70% and the best result I got where: ROC= 0.711 SENS=0.94 SPEC=0.21.I have used classifierplots package in R for a diagnostic plot.this is the plot:I don't really understand how to interrupt the plots of positive instances per decile, prediction density and calibration. I have tried to find a comprehensive explanation with no success.can you help me understand what the graphs mentioned above represent? I really want to improve the results.
interpret diagnostics plots from classifierplots package in R
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