https://github.com/stratosphereips/ml-explorer
A Streamlit app to explore classification and anomaly-detection pipelines on synthetic datasets! 🧪
https://github.com/stratosphereips/ml-explorer
Last synced: 6 months ago
JSON representation
A Streamlit app to explore classification and anomaly-detection pipelines on synthetic datasets! 🧪
- Host: GitHub
- URL: https://github.com/stratosphereips/ml-explorer
- Owner: stratosphereips
- License: gpl-2.0
- Created: 2025-05-18T16:11:01.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-05-18T16:33:28.000Z (about 1 year ago)
- Last Synced: 2025-09-05T06:53:53.951Z (11 months ago)
- Language: Python
- Size: 25.4 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
- Code of conduct: .github/CODE_OF_CONDUCT.md
- Citation: CITATION.cff
- Security: .github/SECURITY.md
Awesome Lists containing this project
README
# 🚀 ML Explorer
A Streamlit app to explore classification and anomaly-detection pipelines on synthetic datasets! 🧪
## 🔍 Features
* **Synthetic Data Generators**:
* `make_classification`, `make_moons`, `make_circles`, `make_blobs`, `make_gaussian_quantiles`
* **Feature Selection**: VarianceThreshold, SelectKBest (ANOVA F-test, Mutual Information), tree-based importance
* **Dimensionality Reduction**: PCA, Kernel PCA, UMAP
* **Scaling**: StandardScaler, MinMaxScaler, RobustScaler
* **Classification & Anomaly Detection**:
* Logistic Regression, SVM, k-NN, Decision Trees, Random/Extra Forests, AdaBoost, GradientBoosting, Bagging, GaussianNB, QDA, MLP, SGD, Passive-Aggressive
* IsolationForest, One-Class SVM, Local Outlier Factor
* **Interactive Metrics**: Confusion matrix counts (TP, TN, FP, FN), Accuracy, Precision, Recall, F1-score, G-Mean, TPR, TNR, FPR, FNR
* **Decision Boundary Visualizations**: 2D plots with fullscreen toggle 📈
## ⚙️ Installation
1. Clone this repo:
```bash
git clone https://github.com/yourusername/ml-explorer.git
cd ml-explorer
```
2. Create a virtual environment (optional but recommended):
```bash
python -m venv venv
source venv/bin/activate # macOS/Linux
venv\Scripts\activate # Windows
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
## 🚀 Usage
Run the Streamlit app:
```bash
streamlit run app.py
```
* Use the sidebar to select dataset, preprocessing steps, and models
* View performance metrics in an interactive table (sortable)
* Expand each model's section to see its decision boundary plot
* Toggle **Fullscreen plots** to enlarge charts inside the main view
## 🤝 Contributing
Feel free to open issues or PRs! ⭐
## 📜 License
MIT License © 2025 eldraco