ml-explorer
A Streamlit app to explore classification and anomaly-detection pipelines on synthetic datasets! 🧪
Science Score: 44.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
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○Academic publication links
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (12.0%) to scientific vocabulary
Repository
A Streamlit app to explore classification and anomaly-detection pipelines on synthetic datasets! 🧪
Basic Info
- Host: GitHub
- Owner: stratosphereips
- License: gpl-2.0
- Language: Python
- Default Branch: main
- Size: 25.4 KB
Statistics
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
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
- 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
Owner
- Name: Stratosphere IPS
- Login: stratosphereips
- Kind: organization
- Location: Prague
- Website: https://www.stratosphereips.org
- Twitter: StratosphereIPS
- Repositories: 25
- Profile: https://github.com/stratosphereips
Cybersecurity Research Laboratory at the Czech Technical University in Prague. Creators of Slips, a free software machine learning-based behavioral IDS/IPS.
Citation (CITATION.cff)
cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "YOUR_NAME_HERE"
given-names: "YOUR_NAME_HERE"
email: youremailhere
affiliation: >-
Stratosphere Laboratory, AIC, FEL, Czech
Technical University in Prague
orcid: "https://orcid.org/0000-0000-0000-0000"
- family-names: "Lisa"
given-names: "Mona"
email: youremailhere
affiliation: >-
Stratosphere Laboratory, AIC, FEL, Czech
Technical University in Prague
orcid: "https://orcid.org/0000-0000-0000-0000"
title: "repository-template"
version: 1.0.0
doi: 10.5281/zenodo.1234
date-released: 2022-07-13
url: "https://github.com/stratosphereips/repository-template"
GitHub Events
Total
- Push event: 1
- Create event: 4
Last Year
- Push event: 1
- Create event: 4
Dependencies
- actions/checkout v2 composite
- anothrNick/github-tag-action 1.36.0 composite
- matplotlib >=3.5.0
- numpy >=1.23.0
- pandas >=1.5.0
- scikit-learn >=1.2.0
- streamlit >=1.25.0
- umap-learn >=0.5.3