File size: 4,940 Bytes
7a464a5 5236cca 41cacc0 5ca5523 5236cca 09edf62 41cacc0 09edf62 41cacc0 09edf62 23c69ec c68cad6 5ca5523 c68cad6 23c69ec 09edf62 41cacc0 c68cad6 41cacc0 09edf62 41cacc0 c68cad6 41cacc0 09edf62 41cacc0 09edf62 41cacc0 09edf62 41cacc0 09edf62 5ca5523 09edf62 41cacc0 09edf62 41cacc0 09edf62 5ca5523 09edf62 5c5ea31 09edf62 41cacc0 5c5ea31 41cacc0 09edf62 41cacc0 09edf62 41cacc0 09edf62 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 |
---
title: School of Statistics
colorFrom: blue
colorTo: indigo
sdk: static
pinned: false
---
# SOS - School of Statistics
[](https://berangerthomas.github.io/SchoolOfStatistics/)
[](https://huggingface.co/spaces/berangerthomas/schoolofstatistics)
[](https://opensource.org/licenses/MIT)
Interactive visualizations for exploring statistical and machine learning concepts. Each page runs entirely in the browser (HTML, CSS, JavaScript with Chart.js) without requiring a server or build step.
## Available Pages
### Classification
- **[Direct Classification](https://berangerthomas-schoolofstatistics.static.hf.space/direct_classifier.html)**: Generate synthetic 2D datasets and observe how class separation affects Gaussian Naive Bayes classifier performance. Displays ROC curve, AUC, confusion matrix, and standard metrics (accuracy, precision, recall, specificity, F1-score).
- **[Inverse Classification](https://berangerthomas-schoolofstatistics.static.hf.space/inverse_classifier.html)**: Directly set confusion matrix values (TP, FP, TN, FN) and observe resulting metrics, ROC curve, and simulated score distributions. Parameters can be locked to constrain totals.
### Regression
- **[Linear Regression](https://berangerthomas-schoolofstatistics.static.hf.space/linear_regression.html)**: Interactive point placement on canvas with linear or polynomial regression fitting. Displays residuals, coefficient of determination (R²), and regression diagnostics. Supports zoom, point dragging, and confidence band display.
### Signal Processing
- **[Fourier Transform](https://berangerthomas-schoolofstatistics.static.hf.space/fourier_transform.html)**: Compose signals from sine waves and visualize their frequency spectrum. Up to 4 components with frequency, amplitude, and phase control. Displays time-domain signal, magnitude spectrum, phase spectrum, and signal metrics (sampling rate, Nyquist frequency, frequency resolution, total power, RMS).
## Project Structure
```
.
├── direct_classifier.html # Direct classification (Naive Bayes)
├── inverse_classifier.html # Inverse classification (confusion matrix)
├── linear_regression.html # Linear/polynomial regression
├── fourier_transform.html # Fourier transform
├── CHANGELOG.md # Version history
├── LICENSE
├── README.md
└── src/
├── assets/
│ └── logo.jpg
├── css/
│ ├── style.css # Shared base styles
│ ├── direct_classifier.css # Page-specific styles
│ ├── embedding_distances.css
│ ├── fourier_transform.css
│ └── linear_regression.css
└── js/
├── common.js # Shared utilities (metrics, ROC, matrices, drag, etc.)
├── direct_classifier.js
├── fourier_transform.js
├── inverse_classifier.js
├── linear_regression.js
└── logistic_regression.js
```
## Usage
1. Clone the repository.
2. Open any `.html` file in your web browser.
No dependencies to install — all libraries are loaded via CDN.
## Versioning
See [CHANGELOG.md](CHANGELOG.md) for release history.
## Roadmap
### Upcoming
- **Bias-Variance Tradeoff Explorer**: visualize bias-variance decomposition with polynomial fitting of increasing degree
- **k-Nearest Neighbors Playground**: interactive point placement and k-NN decision boundary visualization
- **Gradient Descent Visualizer**: real-time navigation on 2D loss surfaces, optimizer comparison
- **Principal Component Analysis (PCA) Step-by-Step**: Gaussian cloud generation and principal component visualization
- **Clustering Algorithms Visualizer**: k-Means and DBSCAN comparison on various dataset shapes
- **Neural Network Architecture & Forward Pass Visualizer**: layer-by-layer fully-connected network construction
- **Tokenization & Embedding Visualizer**: tokenization and 2D embedding space projection
- **Attention Mechanism Visualizer**: Transformer attention mechanism visualization
- **Probability Distributions Explorer**: exploration of standard distributions (Normal, Uniform, Exponential, Poisson, Binomial, Beta, Gamma, Chi-squared)
- **Markov Chain Text Generator**: Markov chain construction and text generation
- **A/B Testing Calculator**: statistical tool for hypothesis testing
- **Voice Signal Waveform Analyzer**: audio recording, waveform display, spectrogram computation, and dominant frequency identification
## License
See the [LICENSE](LICENSE) file.
|