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Decision Tree Analysis Course

Decision Tree Analysis Course
flexible workload of 4 to 360h
valid certificate in your country

What will I learn?

Gain expertise in defining precise high-cost targets, constructing transparent decision trees using Python or R, and fine-tuning hyperparameters for optimal results. Master handling missing data, class imbalances, and outliers, while evaluating models via ROC curves, F1 scores, and calibration methods. Learn to confidently present results, risk segments, and actionable business recommendations to non-technical audiences.

Elevify advantages

Develop skills

  • Build interpretable CART decision tree models swiftly in Python and R.
  • Optimise tree depth, pruning, and class weights to achieve superior performance metrics.
  • Apply cost-sensitive thresholds using AUC, F1 scores, and business impact analysis.
  • Perform transparent data preprocessing, encoding, and feature engineering for robust trees.
  • Convert decision tree outputs into clear pricing strategies and risk management insights for stakeholders.

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.
Workload: between 4 and 360 hours

What our students say

I was just promoted to Intelligence Advisor of the Prison System, and the course from Elevify was crucial for me to be chosen.
EmersonPolice Investigator
The course was essential to meet the expectations of my boss and the company I work for.
SilviaNurse
Very great course. Lots of rich information.
WiltonCivil Firefighter

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