How to describe the architecture of a random forest model with an added linear model for extrapolation?

The architecture of the model that combines a random forest and a linear model for extrapolation is based on leveraging the advantages of both models:

• Random Forest handles complex nonlinear dependencies well and is resistant to overfitting, but poorly extrapolates beyond the training data.
• Linear Model provides a simple and interpretable approximation, capable of extrapolation, but is limited in describing complex dependencies.

Architecture description:

1. Features are input.
2. The Random Forest is trained on the data and predicts values in a region close to the training set.
3. The Linear Model is trained on the same data but is used for predictions outside the training data region (extrapolation).
4. For new input data, the system decides whether to use the Random Forest prediction (if data is within the training region) or the Linear Model (if data falls outside the training region).

Thus, the architecture represents a hybrid model with conditional switching between the Random Forest and the Linear Model, allowing for high accuracy in interpolation and adequate extrapolation.