|
|
--- |
|
|
--- |
|
|
title: School of Statistics - Interactive Classification Dashboards |
|
|
emoji: 📊 |
|
|
colorFrom: blue |
|
|
colorTo: indigo |
|
|
sdk: static |
|
|
pinned: false |
|
|
--- |
|
|
|
|
|
# School of Statistics - Interactive Classification Dashboards |
|
|
|
|
|
 |
|
|
|
|
|
Welcome to the Interactive Classification Dashboards project! This repository contains a set of tools designed to help users understand the core concepts of binary classification in machine learning through hands-on, visual interaction. |
|
|
|
|
|
## 🚀 About This Project |
|
|
|
|
|
### 🌐 Live Demos |
|
|
|
|
|
* **[Direct Classification Dashboard](https://berangerthomas.github.io/SchoolOfStatistics/direct_classifier.html)** |
|
|
* **[Inverse Classification Dashboard](https://berangerthomas.github.io/SchoolOfStatistics/inverse_classifier.html)** |
|
|
|
|
|
This project provides two distinct interactive dashboards: |
|
|
|
|
|
1. **Direct Classification Dashboard (`direct_classifier.html`)** : This tool allows you to generate a synthetic 2D dataset for two classes. You can adjust the **class separation** and **data spread (standard deviation)** to see how these parameters affect the performance of a Gaussian Naive Bayes classifier. The dashboard visualizes: |
|
|
* The generated data points. |
|
|
* The resulting ROC curve and its Area Under the Curve (AUC). |
|
|
* Key performance metrics (Accuracy, Precision, Recall, etc.). |
|
|
* A detailed confusion matrix. |
|
|
|
|
|
2. **Inverse Classification Dashboard (`inverse_classifier.html`)** : This tool works in reverse. Instead of generating data, you directly manipulate the values of the **confusion matrix** (True Positives, False Positives, True Negatives, and False Negatives). The application then simulates a distribution of classifier scores that would lead to your specified matrix and visualizes the resulting metrics, ROC curve, and score distribution. This provides a unique, intuitive way to understand the relationships between the confusion matrix and other performance indicators. |
|
|
|
|
|
## 📂 Project Structure |
|
|
|
|
|
The project has been organized into a clean and maintainable structure: |
|
|
|
|
|
``` |
|
|
. |
|
|
├── direct_classifier.html |
|
|
├── inverse_classifier.html |
|
|
├── LICENSE |
|
|
├── README.md |
|
|
└── src |
|
|
├── assets |
|
|
│ └── logo.jpg |
|
|
├── css |
|
|
│ ├── inverse_style.css |
|
|
│ └── style.css |
|
|
└── js |
|
|
├── direct_classifier.js |
|
|
└── inverse_classifier.js |
|
|
``` |
|
|
|
|
|
* **`direct_classifier.html`**: The main page for the direct classification tool. |
|
|
* **`inverse_classifier.html`**: The main page for the inverse classification tool. |
|
|
* **`src/`**: Contains all source assets. |
|
|
* **`assets/`**: Stores static assets like the project logo. |
|
|
* **`css/`**: Contains the stylesheets for the HTML pages. |
|
|
* **`js/`**: Contains the JavaScript logic for each interactive dashboard. |
|
|
* **`LICENSE`**: The project's license file. |
|
|
* **`README.md`**: This file. |
|
|
|
|
|
## 🛠️ How to Use |
|
|
|
|
|
1. Clone this repository to your local machine. |
|
|
2. Open either `direct_classifier.html` or `inverse_classifier.html` in your web browser. |
|
|
3. No local server is needed! All the logic is self-contained in the HTML, CSS, and JavaScript files. |
|
|
|
|
|
Interact with the sliders and controls on each page to explore the concepts of classification. |
|
|
|
|
|
## 📄 License |
|
|
|
|
|
This project is distributed under the terms of the license specified in the `LICENSE` file. |
|
|
|
