Random_Forest_Implementation

The goal of this repository is to create an implementation in Python of the Random Forest algorithm

Build Status

For the moment, the first version of the model is coded and ready to be used

However, it is still slow for the moment and the next steps is to reduce time complexity while making it more user friendly.

Throughout its construction, if you see any improvements that could be made in the code, do not hesitate to reach out at Hippolyte.guigon@hec.edu

Code style

The all project is coded under PEP-8 (https://peps.python.org/pep-0008/) and flake8 (https://pypi.org/project/flake8/) compliancy. Such compliance is verified during commits with pre-commits file .pre-commit-config.yaml

Installation

• This project uses a specific conda environment, to get it, run the following command: conda env create -f random_forest_env.yml

• To install all necessary libraries, run the following code:pip install -r requirements.txt

• This project has its own package that is used. To get it, run the following command: python install setup.py

Screenshot

Image of a Random Forest decision process

How to use ?

1. To import the model, run the following command: from Random_forest.model.model import RandomForest

2. Then, use model=RandomForest() and adjust hyperparameters according to your goals. Not that if you want to use RandomForest for regression, you'll have to set objective="regression" and objective="classification" according to which suprvised method you wish to use

3. Run model.fit(X, y)

4. Predict your data with model.predict(X_to_predict)