Further Classification Methods: Decision Trees and Random Forests

Decision trees and random forests have a number of uses in finance, from card fraud and loan default protection through to personnel retention.

I In addition to practical implementation in Python, this course explores methodological considerations such as the disproportionate rareness of ‘positive’ cases and how this affects model validity.


This course is suitable for: Tech Analysts, S&T Analysts (optionally).

Learning Outcomes

By the end of this course, participants will be able to:

  • Compare and contrast random forest and decision tree techniques
  • Implement these techniques using standard libraries
  • Comment on the efficiency of these methods given limitations in datasets

Course Content

  • Introduction: supervised and unsupervised machine learning, decision trees and random forests
  • Implement decision tree in Scikit-Learn (there is no TF implementation to draw on!)
  • Implement random forest in TensorFlow (TF does do Random Forests)
  • Conclusions: which is the better method? When, and why?


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