Machine Learning with Tree-Based Models in R
Engineering and Technology
Short Description
Learn how to use tree-based models and ensembles to make classification and regression predictions with tidymodels.
Long Description
This course focuses on the utilization of tree-based machine learning models to uncover intricate non-linear relationships within data, which frequently outperform other models in machine learning competitions. By employing the tidymodels package, participants will gain proficiency in examining and constructing various tree-based models, ranging from basic decision trees to intricate random forests. Additionally, the course will delve into the implementation of boosted trees, a potent machine learning technique that leverages ensemble learning to construct highly effective predictive models. Throughout the course, participants will engage with health and credit risk data to forecast the likelihood of diabetes occurrence and customer churn, thereby enhancing their practical understanding of these concepts.
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Course Details
- Duration
- 4 hours
- Format
- Short Course
- Price
- USD39.00
- Course Link More Information