Accuracy = % of classifications we label correctly.

This is very interesting. Intuitively, it might make sense that we just want to maximize accuracy when training a model. However, we usually don’t want to do that in Classification, because it can lead to some really bad decisions.

Why Accuracy is bad

When we use accuracy, we assign equal cost to false positives and false negatives. When that data set is imbalanced - say it has 99% of instances in one class and only 1 % in the other - there is a great way to lower the cost. Predict that every instance belongs to the majority class, get accuracy of 99% and go home early.

The problem starts when the actual costs that we assign to every error are not equal. If we deal with a rare but fatal disease, the cost of failing to diagnose the disease of a sick person is much higher than the cost of sending a healthy person to more tests.

In general, there is no general best measure. The best measure is derived from your needs. In a sense, it is not a machine learning question, but a business question. It is common that two people will use the same data set but will choose different metrics due to different goals.

Accuracy is a great metric. Actually, most metrics are great and I like to evaluate many metrics. However, at some point you will need to decide between using model A or B. There you should use a single metric that best fits your need.

For extra credit, choose this metric before the analysis, so you won’t be distracted when making the decision.