DataCamp
FreeMachine Learning with Tree-Based Models in R
4.9(262)10k studentsBEGINNEREnglish
Korshub's take
The Korshub Take
This beginner-friendly course teaches how to build powerful tree-based models in R using the modern tidymodels ecosystem. You will apply decision trees, random forests, and boosted trees to predict outcomes on health and risk datasets.
Who It's For
R beginners and data analysts looking to build non-linear predictive models using tidymodels and tree ensembles.
Key Takeaways
- tidymodels Framework: Use R's tidymodels package to train, tune, and evaluate tree-based models.
- Random Forests & Gradient Boosting: Harness ensemble techniques like random forests and boosted trees to boost predictive accuracy.
- Real-World Risk Modeling: Practice on health data to predict diabetes incidence and credit risk data to forecast customer churn.
About this course
Tree-based machine learning models can reveal complex non-linear relationships in data and often dominate machine learning competitions. In this course, you'll use the tidymodels package to explore and build different tree-based models—from simple decision trees to complex random forests. You’ll also learn to use boosted trees, a powerful machine learning technique that uses ensemble learning to build high-performing predictive models. Along the way, you'll work with health and credit risk data to predict the incidence of diabetes and customer churn.
Price
Free
This course is free to enrol.
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