R-squared

R-squared, also called the coefficient of determination, is another method of representing how accurate the prediction is compared to the test set. R-squared is calculated by taking the sum of the distance between every data point and the mean squared, subtracting them and then squaring it.

R-squared values generally range between 0 and 1, represented as a floating-point value. A negative value can occur when the fitted model is evaluated to be worse than an average fit. However, a low number does not always reflect that the model is bad. Predictions such as the one we looked at in this chapter that is based on predicting human actions are often found to be under 50%. 

Conversely, higher values aren't necessarily a sure sign of the model's performance, as this could be considered an overfitting of the model. This happens in cases when there are a lot of features fed to the model, thereby making the model more complex than, for instance, the model we built in Chapter 1, Getting Started with Machine Learning and ML.NET, or there is simply not enough diversity in the training and test sets. For example, if all of the employees were roughly the same values, and the test set holdout was comprised of the same ranges of values, this would be considered overfitting.