Tree-based models such as decision trees, random forests, and boosted trees provide powerful predictions and are fast to compute. There are many different ways to fit these models in R, including the rpart, randomForest, and xgboost packages. During this talk, we’ll examine numerous ways to fit each of these model types (and more!) and compare them based on user-friendliness, accuracy, and speed.
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