When we develop or implement a particular ML algorithm, we need to estimate how well it works. In other words, we need to estimate how well it solves our task. Usually, we use some numeric metrics for algorithm performance estimation. An example of such a metric could be a value of mean squared error that's been calculated for target and predicted values. We can use this value to estimate how distant our predictions are from the target values we used for training. Another use case for performance metrics is their use as objective functions in optimization processes. Some performance metrics are used for manual observations, though others can be used for optimization purposes too.

Performance metrics are different for each of the ML algorithms types. In Chapter 1, Introduction to Machine Learning with C++, we discussed that two main categories of ML algorithms exist: regression algorithms and classification algorithms. There are other types of algorithms in the ML discipline, but these two are the most common ones. This section will go over the most popular performance metrics for regression and classification algorithms.