Decision Tree Analysis Course
This course provides hands-on training in decision tree analysis, covering model construction, tuning, evaluation, and communication of results for business applications.

from 4 to 360h flexible workload
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 confidently share results, risk segmentation, and practical business recommendations with non-technical stakeholders.
Elevify advantages
Develop skills
- Decision tree modelling: quickly build understandable CART models 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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