https://github.com/t-lak/decision-tree
This project implements a basic Decision Tree classifier. It supports visualizing the tree and calculating performance metrics (accuracy, precision, F1-score, and recall).
https://github.com/t-lak/decision-tree
decision-trees graphviz metrics numpy python3 scikit-learn
Last synced: about 2 months ago
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This project implements a basic Decision Tree classifier. It supports visualizing the tree and calculating performance metrics (accuracy, precision, F1-score, and recall).
- Host: GitHub
- URL: https://github.com/t-lak/decision-tree
- Owner: T-Lak
- License: mit
- Created: 2025-02-05T15:26:17.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2025-02-13T01:27:19.000Z (over 1 year ago)
- Last Synced: 2025-02-13T02:28:10.830Z (over 1 year ago)
- Topics: decision-trees, graphviz, metrics, numpy, python3, scikit-learn
- Language: Python
- Homepage:
- Size: 25.4 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Overview
This project implements a basic Decision Tree classifier. It is designed to perform classification tasks on datasets with labeled features.
## Features
- **📄 Tree-Visualization:** Decision Tree will be stored as PDF file
- **📊 Metrics:** Accuracy, Precision, F1-Score and Recall
- **🧮 Matrices:** Confusion Matrix and Multilabel Matrices
## Usage
```bash
python3 dtc_cli.py dataset_name.csv
```
## Dependencies
- Python 3.x
- NumPy
- Pandas
- Graphviz
- Scikit-learn
## Installation
To install the required dependencies, run:
```bash
pip install -r requirements.txt
```
## License
This project is licensed under the MIT License.