MLExplain — Interactive Machine Learning Model Explainer
Machine Learning
An educational tool for exploring ML algorithms. Train models with a click, inspect feature importance, study confusion matrices, and compare algorithms side by side.
Live Preview
Interactive preview — open in full window for the best experience
Technology Stack
Python 3.11Flask 3.0scikit-learnChart.jsSQLAlchemyDocker
Key Features
- Built-in Datasets — Iris, Wine, Breast Cancer, Digits from scikit-learn
- Five Algorithms — Decision Tree, Random Forest, SVM, KNN, Logistic Regression
- Hyperparameters — Configurable max depth, n_estimators, C, k, solver
- Metrics Dashboard — Accuracy, Precision, Recall, F1-Score per experiment
- Feature Importance — Tree-based and permutation importance bar charts
- Confusion Matrix — Interactive heatmap with per-class counts
- Model Comparison — Train multiple models, compare metrics side by side
- Prediction API — POST features, receive prediction with confidence scores
About This Project
Fully open source (MIT License), containerized with Docker, and includes a CI/CD pipeline via GitHub Actions. Demonstrates production-grade Python development with comprehensive test coverage, clean architecture, and professional deployment.