How to use the Random Forest Regression tool: WhiteboxTools Tutorial

In this video, I show you how to run one of the new machine learning tools in the Whitebox General Toolset Extension, Random Forest Regression. This tool performs a supervised random forest (RF) regression analysis using multiple predictor rasters, or features, and training data. It can be used to model the spatial distribution of continuous data, such as soil properties (e.g. percent sand/silt/clay). Random forest is an ensemble learning method that works by creating a large number of decision trees and using an averaging of each tree to determine estimated outcome values. Individual trees are created using a random sub-set of predictors. This tool requires a software license from Whitebox Geospatial, which you can find for information about on our website (https://www.whiteboxgeo.com/extension-pricing/). Read more about this tool in the WhiteboxTools Manual (https://www.whiteboxgeo.com/manual/wbt_book/available_tools/machine_learning.html#RandomForestRegression). Soil data provided by the Ontario Ministry of Agriculture, Food and Rural Affairs (OMAFRA). Please comment any questions you have down below or visit our Google Group. Please like this video and subscribe to our YouTube channel for all news and updates. Check out this video and more tutorials on our website (https://www.whiteboxgeo.com/learning-resources/).