Decision Tree Analysis Course
This course teaches decision tree analysis for building interpretable models, tuning performance, handling data issues, evaluating with key metrics, and communicating insights to stakeholders.

from 4 to 360h 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 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 are saying
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