# Model Development We used the random forest classifier ([Breiman 2001](https://link.springer.com/article/10.1023/a:1010933404324)) implemented in Google Earth Engine (GEE) to predict tree height (m), tree diameter (cm), and canopy cover (%) from the [covariates](/terradapt-cascadia-documentation/methods-and-validation/forest-structure/covariates.md) at all [training data](/terradapt-cascadia-documentation/methods-and-validation/forest-structure/training-data.md) locations. We trained 10 replicate models in regression mode per variable in a k-fold approach ([Fushiki 2011](https://link.springer.com/article/10.1007/s11222-009-9153-8)) with a unique 'fold' of 1/10th of the data withheld for [validation](/terradapt-cascadia-documentation/methods-and-validation/forest-structure/model-validation.md). The final model was calculated as the average of the 10 replicates. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://terradapt.gitbook.io/terradapt-cascadia-documentation/methods-and-validation/forest-structure/model-development.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.