![]() ![]() Decision trees seek to find the best split to subset the data, and they are typically trained through the Classification and Regression Tree (CART) algorithm. Observations that fit the criteria will follow the “Yes” branch and those that don’t will follow the alternate path. Each question helps an individual to arrive at a final decision, which would be denoted by the leaf node. ![]() These questions make up the decision nodes in the tree, acting as a means to split the data. Decision trees start with a basic question, such as, “Should I surf?” From there, you can ask a series of questions to determine an answer, such as, “Is it a long period swell?” or “Is the wind blowing offshore?”. Since the random forest model is made up of multiple decision trees, it would be helpful to start by describing the decision tree algorithm briefly. Its ease of use and flexibility have fueled its adoption, as it handles both classification and regression problems. Random forest is a commonly-used machine learning algorithm trademarked by Leo Breiman and Adele Cutler, which combines the output of multiple decision trees to reach a single result. Learn about the random forest algorithm and how it can help you make better decisions to reach your business goals.
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