ysraell/random-forest-mc
Random Forest with Dynamic Tree Selection Monte Carlo Based (RF-TSMC).
This project helps data analysts and scientists classify events or behaviors from structured datasets, even with imbalanced classes or missing information. You feed it a dataset with various features and a target outcome (like 'churn' or 'survived'), and it produces a model that predicts the target. It's designed for anyone needing robust, explainable predictions without extensive data cleaning.
Available on PyPI.
Use this if you need to predict categories (like 'yes/no' or 'A/B/C') from complex, real-world data that might have missing values or uneven distributions across categories.
Not ideal if your primary goal is numerical prediction (like forecasting a stock price) rather than categorizing outcomes.
Stars
51
Forks
2
Language
Python
License
MIT
Category
Last pushed
Nov 24, 2025
Commits (30d)
0
Dependencies
3
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