What do the statistics mean?

Mean Squared Error: The average squared difference between actual and predicted values
|    - The higher the value, the less accurate the model is.
R2 (R-Squared): How well the regression tree fits the data.
|    - The closer to 1, the better
|    - Despite my models having a high R2, this does not directly equate to it's accuracy. There are more factors that go into SR and the SR economy will always change.

Why two models?
Initially, I only had the Random Forest model, which was good for what it's meant for: interpolation, guessing a value within the training data. However, going outside the SR range for my data resulted in no extrapolation, guessing a value outside of the training data.

This meant that the model was really good at returning accurate results for the 2K-17K range, but not good for anything outside of that. The model would never return values under 2K or above 17K. Therefore, I decided to introduce a model that can "guess" values outside the training data's range.

Rather than replace the model, I decided to have two models instead, switching to the alternate model which supports extrapolation when needed.

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