Decision Tree Analysis Course
This course equips learners with practical skills in decision tree analysis, focusing on building, tuning, and evaluating models for real-world applications in risk assessment and business decision-making.

4 to 360 hours of flexible workload
certificate valid in your country
What Will I Learn?
This Decision Tree Analysis Course teaches you how to set a clear high-cost target, construct transparent trees using Python or R, and adjust key hyperparameters for dependable performance. You will manage missing data, class imbalance, and outliers, assess models with ROC, F1, and calibration methods, and present results, risk segmentation, and business-ready recommendations to non-technical stakeholders confidently.
Elevify Advantages
Develop Skills
- Decision tree modelling: build interpretable CART models quickly in R and Python.
- Performance tuning: optimise depth, pruning, and class weights for precise metrics.
- Cost-sensitive evaluation: establish thresholds using AUC, F1, and business impact.
- Transparent preprocessing: clean, encode, and engineer features for clear trees.
- Stakeholder reporting: convert tree outputs into concise pricing and risk insights.
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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