random-forest-mc 0.0.1

This project is about use Random Forest approach using a dynamic tree selection Monte Carlo based.

Random Forest with Dyanmic Tree Selection Monte Carlo Based (RF-TSMC)

This project is about use Random Forest approach using a dynamic tree selection Monte Carlo based. The first implementation is found in [2] (using Common Lisp).

References

[2] Laboratory of Decision Tree and Random Forest (github/ysraell/random-forest-lab). GitHub repository.

[3] Credit Card Fraud Detection. Anonymized credit card transactions labeled as fraudulent or genuine. Kaggle. Access: https://www.kaggle.com/mlg-ulb/creditcardfraud.

Notes

• Python requirements in requirements.txt. Better for Python >=3.7. Run the follow command inside this repository:

$ pip3 install -r requirements.txt --no-cache-dir

Development Framework (optional)

• My data science Docker image.

With this image you can run all notebooks and scripts Python inside this repository.

TODO:

• Implement the code.
  • [Plus] Add a method to return the list of feaures and their degrees of importance.
• Set Poetry and publish to PyPI.
• Add parallel processing using or TQDM or csv2es style.