Decision Tree Analysis Course
This comprehensive course equips you with skills to excel in decision tree analysis for cost prediction in real-world scenarios. You will master data preparation, model tuning, managing class imbalances, performing AUC-ROC evaluations, and effectively communicating insights to stakeholders, transforming intricate risk patterns into practical business strategies that drive informed decisions.
flexible workload of 4 to 360h
valid certificate in your country
What will I learn?
Gain expertise in defining precise high-cost targets, constructing transparent decision trees using Python or R, and fine-tuning hyperparameters for optimal results. Master handling missing data, class imbalances, and outliers, while evaluating models via ROC curves, F1 scores, and calibration methods. Learn to confidently present results, risk segments, and actionable business recommendations to non-technical audiences.
Elevify advantages
Develop skills
- Build interpretable CART decision tree models swiftly in Python and R.
- Optimise tree depth, pruning, and class weights to achieve superior performance metrics.
- Apply cost-sensitive thresholds using AUC, F1 scores, and business impact analysis.
- Perform transparent data preprocessing, encoding, and feature engineering for robust trees.
- Convert decision tree outputs into clear pricing strategies and risk management insights for stakeholders.
Suggested summary
Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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