So many concerns about this one... - what is the metric exactly? - what does 90% mean, is it bad or good? That depends on the baseline and the prior distribut

So many concerns about this one... - what is the metric exactly? - what does 90% mean, is it bad or good? That depends on the baseline and the prior distributions. - on what dataset is the 90% computed? On past crimes? What about sampling bias in this dataset? What if the data focuses on some specific neighborhoods (oh wait...)? What about missing crimes in the data? In ML, data should fairly well represent a phenomenon and in this case, it does not represent crime occurrence, it only represents crimes detected and documented.