Decision Tree Analysis Course
This course equips learners with practical skills in decision tree analysis, from building models in Python or R to evaluating and communicating 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, build open trees in Python or R, and adjust main hyperparameters for steady performance. You will manage missing data, class imbalance, and outliers, assess models with ROC, F1, and calibration tools, and share results, risk segmentation, and business-ready recommendations to non-technical stakeholders with confidence.
Elevify advantages
Develop skills
- Decision tree modeling: build interpretable CART models fast in R and Python.
- Performance tuning: optimize 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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