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

4 to 360 hours flexible workload
valid certificate in your country
What will I learn?
This Decision Tree Analysis Course shows you how to define a clear high-cost target, build transparent trees in Python or R, and tune key hyperparameters for reliable performance. You will handle missing data, class imbalance, and outliers, evaluate models with ROC, F1, and calibration tools, and communicate results, risk segmentation, and business-ready recommendations to non-technical stakeholders with confidence.
Elevify advantages
Develop skills
- Decision tree modelling: build interpretable CART models fast in R and Python.
- Performance tuning: optimise depth, pruning, and class weights for sharp metrics.
- Cost-sensitive evaluation: set thresholds using AUC, F1, and business impact.
- Transparent preprocessing: clean, encode, and engineer features for clear trees.
- Stakeholder reporting: turn 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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