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.

Suitability

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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