Through step-by-step coding practices, learners will implement decision tree algorithms using R packages like rpart and tree, visualize results, and evaluate performance with tools such as the confusion matrix. They will also learn to generate actionable insights for decision-making, with a particular emphasis on financial risk management applications.
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Through step-by-step coding practices, learners will implement decision tree algorithms using R packages like rpart and tree, visualize results, and evaluate performance with tools such as the confusion matrix. They will also learn to generate actionable insights for decision-making, with a particular emphasis on financial risk management applications.
Through step-by-step coding practices, learners will implement decision tree algorithms using R packages like rpart and tree, visualize results, and evaluate performance with tools such as the confusion matrix. They will also learn to generate actionable insights for decision-making, with a particular emphasis on financial risk management applications.
This course is uniquely designed to bridge theory with practice, combining structured progression for beginners with advanced applications for intermediate learners. By completing it, participants will not only master supervised learning with decision trees but also confidently apply their models to real-world business and financial scenarios, strengthening both their machine learning expertise and analytical decision-making skills.
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Syllabus
Foundations of Decision Tree Modeling
This module introduces learners to the fundamentals of decision tree modeling using R. It covers the basics of tree structure, data preparation, and the creation of classification models. By the end of this module, learners will understand how to preprocess data, construct decision trees, and evaluate model performance effectively.
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