Explore various tree-based models for powerful predictions in this comprehensive video lecture. Learn about decision trees, random forests, and boosted trees, comparing their implementation in R using packages like rpart, randomForest, and xgboost. Examine different fitting methods for each model type, evaluating them based on user-friendliness, accuracy, and speed. Discover the intricacies of single trees, random forests, and boosted trees, along with specific packages like ranger, gbm, C5.0, xgboost, and lightgbm. Gain insights into feature engineering with recipes, workflows, and model fitting using tidymodels. Analyze the performance of different models and extract valuable takeaways for practical application in data science projects.