Model evaluation is an important part of a data science project and it's exactly this part that quantifies how good your model is, how much it has improved from the previous version, how much better it is than your colleague's model, and how much room for improvement there still is. It is not unusual in machine learning applications to deal with imbalanced datasets such as fraud detection, computer network intrusion, medical diagnostics, and many more. Data imbalance refers to unequal distribution of classes within a dataset, namely that there are far fewer events in one class in comparison to the others. If, for example we have credit card fraud detection dataset, most of the transactions are not fraudulent and very few can be classed as fraud detections. This underrepresented class is called the minority class, and by convention, the positive class.

It's no secret that the U.S. spends a lot on health care, around 18 percent of its GDP or $9,400 per capita, nearly double what other high-income countries such as Canada, UK, Germany, and Australia spend. But more spending doesn't necessarily yield better results. In fact, studies show that many of the countries that spend less than the U.S. see better outcomes in the overall health of their citizens. According to a new report published by the Journal of the American Medical Association (JAMA), a little less than half the health care expenditures in the U.S. go into planning, regulating, and managing medical services at the administrative level. And industry experts believe we can reduce a lot of this spending with the help of artificial intelligence.