AntoinePinto/custom-decision-trees
A package for building customizable decision trees and random forests.
This project helps data scientists, analysts, and domain experts build decision tree models that prioritize specific business goals like maximizing profit or minimizing risk, instead of just accuracy. You feed in your dataset and define what 'success' means for your problem, and the tool outputs a decision tree optimized for that custom criteria, along with predictions. This is ideal for anyone in fields like finance, marketing, or fraud detection where the cost of different errors varies greatly.
No commits in the last 6 months. Available on PyPI.
Use this if you need to build predictive models where standard metrics like accuracy aren't enough, and you want to explicitly optimize for a custom, business-driven goal.
Not ideal if you're looking for a simple, out-of-the-box decision tree with standard accuracy or Gini impurity optimization.
Stars
10
Forks
3
Language
Python
License
MIT
Category
Last pushed
Oct 06, 2025
Commits (30d)
0
Dependencies
3
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