Discover top open-source software, updated regularly with real-world adoption signals.
Automated, transparent machine learning for tabular data in minutes
mljar-supervised automates preprocessing, model selection, hyper‑parameter tuning, and reporting for tabular datasets, delivering transparent pipelines and visual explanations in minutes.
mljar-supervised is a Python package that streamlines the end‑to‑end workflow for tabular machine‑learning projects. It targets data scientists, analysts, and developers who need fast baselines, thorough model comparisons, and clear documentation without writing extensive boilerplate code.
The library offers four built‑in modes—Explain, Perform, Compete, and Optuna—each tuned for different goals such as data exploration, production‑ready pipelines, competition‑level performance, or exhaustive hyper‑parameter search. It automatically handles missing values, categorical encoding, and advanced feature engineering (e.g., golden features, text and time transforms). A wide algorithm suite (Linear, Decision Tree, Random Forest, LightGBM, XGBoost, CatBoost, Neural Networks, etc.) is combined with greedy ensembling and optional stacking. Every run generates a detailed Markdown report with learning curves, feature importance, SHAP visualizations, and model metrics, enabling reproducibility and auditability. The optional web‑app provides a code‑free GUI for secure local execution.
When teams consider MLJAR, these hosted platforms usually appear on the same shortlist.
Cloud service for accelerating and managing the machine learning project lifecycle, including training and deployment of models
Automated machine learning platform for building AI models without coding
Unified ML platform for training, tuning, and deploying models
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Quick baseline generation
Produces a set of candidate models with performance metrics and a ready‑to‑use report within minutes.
Explainability audit for compliance
Delivers SHAP plots, decision‑tree visualizations, and feature‑importance charts to satisfy regulatory review.
ML competition entry
Creates a stacked ensemble with cross‑validated scores, maximizing leaderboard performance.
Automated monthly analysis
Generates reproducible Markdown reports for each run, enabling consistent documentation across cycles.
It works with tabular datasets containing numeric, categorical, text, and time‑series features.
Run `pip install mljar-supervised` in your Python environment.
Yes, all training and reporting can be performed locally; the optional web UI runs on your machine.
Explain, Perform, Compete, and Optuna, each optimized for exploration, production, competition, or exhaustive tuning.
Depending on the mode, the library uses train/test splits or k‑fold cross‑validation and reports metrics such as accuracy, F1, ROC‑AUC, and more.
Project at a glance
StableLast synced 4 days ago
