Azure Machine Learning
Cloud service for accelerating and managing the machine learning project lifecycle, including training and deployment of models
MLJAR
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.
Overview
Overview
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.
Capabilities
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.
Highlights
Four purpose‑driven modes (Explain, Perform, Compete, Optuna)
Automatic preprocessing, feature engineering, and hyper‑parameter tuning
Greedy ensembling and optional stacking for top performance
Comprehensive Markdown reports with visual explanations
Pros
- Speeds up model development for tabular data
- Provides transparent pipelines and detailed documentation
- Built‑in explainability with SHAP and decision‑tree visualizations
- Supports a broad range of algorithms and ensembling
Considerations
- Focused on tabular data; not suitable for image or audio tasks
- Optuna mode can be computationally intensive for large datasets
- Requires Python environment and compatible libraries (e.g., LightGBM)
- Advanced feature engineering may increase runtime on very large data
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Fit guide
Great for
- Rapid prototyping of predictive models on structured data
- Regulatory or business audits that need model explainability
- Machine‑learning competition participants seeking stacked ensembles
- Teams that want automated, reproducible reporting for recurring analyses
Not ideal when
- Computer‑vision or natural‑language processing projects
- Real‑time streaming inference with strict latency constraints
- Datasets that exceed available memory without out‑of‑core support
- Users requiring custom deep‑learning architectures beyond provided models
How teams use it
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.
Tech snapshot
Python100%
Frequently asked questions
What types of data does mljar-supervised support?
It works with tabular datasets containing numeric, categorical, text, and time‑series features.
How do I install the package?
Run `pip install mljar-supervised` in your Python environment.
Can I use the library without an internet connection?
Yes, all training and reporting can be performed locally; the optional web UI runs on your machine.
What are the available AutoML modes?
Explain, Perform, Compete, and Optuna, each optimized for exploration, production, competition, or exhaustive tuning.
How are models evaluated?
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
Stable- Stars
- 3,234
- Watchers
- 3,234
- Forks
- 430
LicenseMIT
Repo age7 years old
Last commit7 months ago
Primary languagePython
Last synced 12 hours ago