This repository contains the implementation of a Random Forest classifier, developed as a final project for Math concepts for developers course.
The main goal of this project is to apply supervised learning techniques to classify data using the Random Forest algorithm. The implementation demonstrates a solid understanding of ensemble methods and decision tree-based classification.
- Custom implementation or use of
sklearn.ensemble.RandomForestClassifier - Data preprocessing and feature selection
- Model training and evaluation
- Performance metrics (accuracy, precision, recall, F1-score)
- Visualization of results (optional)
Random Forest is an ensemble learning method that builds multiple decision trees and merges their results to improve classification accuracy and control overfitting. It is robust, scalable, and widely used in real-world applications.
project/ ├── data/ # Input datasets ├── img/ # Source img files ├── Project/ # Main project ├── RF_Demo/ # RF model demo ├── Source/ # File with information about the Random forest algorithm └── README.md # Project description
How to Run:
- Clone the repository:
git clone https://github.com/plamensve/RANDOM_FOREST_ALGORITHM.git