Decision Tree Analysis Course
Master decision tree techniques for accurate cost forecasting in practical settings. Cover thorough data cleaning, model optimisation, handling imbalanced data, AUC-ROC assessments, and straightforward reporting to transform intricate risk data into valuable business strategies that drive decisions.
from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
Gain skills to set precise high-cost targets, construct clear decision trees using Python or R, and adjust hyperparameters for strong results. Manage missing values, unbalanced classes, and outliers effectively. Assess models via ROC curves, F1 scores, and calibration methods. Present findings, risk profiles, and practical business advice confidently to non-experts.
Elevify advantages
Develop skills
- Build fast, understandable CART decision tree models in Python and R.
- Optimise tree depth, pruning, and class weights to boost performance metrics.
- Apply cost-aware thresholds with AUC, F1 scores, and business outcomes.
- Prepare data cleanly by handling missing values, encoding, and feature creation.
- Convert model results into clear risk and pricing reports for stakeholders.
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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