Decision Tree Analysis Course
Master decision tree analysis for transparent, cost-sensitive modeling and stakeholder communication using Python and R.

flexible workload from 4 to 360h
valid certificate 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 results. You will manage missing data, class imbalance, and outliers, assess models with ROC, F1, and calibration methods, and share results, risk segmentation, and business recommendations with non-technical stakeholders confidently.
Elevify advantages
Develop skills
- Decision tree modeling: build interpretable CART models quickly in R and Python.
- Performance tuning: optimise depth, pruning, and class weights for precise 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 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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