Python library for decision tree visualization and model interpretation.
Open source
24 alternativesmljar-supervised
mljar-supervised provides tabular data AutoML with feature engineering, hyperparameter tuning, explanations, and documentation.
About mljar-supervised
mljar-supervised is an open-source Python package for AutoML on tabular data. It automates data preprocessing, algorithm selection, and hyper-parameter tuning, and produces Markdown reports that show how each ML pipeline was constructed.
It provides four modes: Explain, Perform, Compete, and Optuna. It also can generate a Mercury-based web app from a trained model for single predictions and batch predictions from CSV files.
Key features
- Tests Baseline, Linear, Random Forest, Extra Trees, LightGBM, XGBoost, CatBoost, Neural Networks, Nearest Neighbors
- Automated preprocessing including missing values and categorical conversion
- Hyperparameter tuning with Explain, Perform, Compete, and Optuna modes
- Model explanations with SHAP, permutation importance, and decision tree visualization
- Automatic Markdown reports detailing each trained model and pipeline
- Mercury-based web app generation for single and batch CSV predictions
Best for: Data scientists working with tabular data who need automated modeling with inspectable reports.
Good for
- Shows pipeline construction in detailed Markdown reports for each model
- Supports multiple algorithms with greedy ensemble and level 2 stacking
- Can generate a prediction web app for use without Python code
Watch out for
- Designed for tabular data
- Level 2 stacking is available in Compete mode or with stack_models set
- Optuna mode is available from version 0.10.0 onward
mljar-supervised at a glance
- Category
- AI & Machine Learning › AI Data & Analytics
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
mljar-supervised alternatives
Tools and sites you could use instead, ranked by how closely they match.
Little Ball of Fur provides graph sampling extensions for NetworKit and NetworkX.
Open source
24 alternatives
oneDAL is a C++ and DPC++ library for accelerated machine learning routines on tabular data, governed by the UXL Foundation.
Open source
24 alternatives
The Featuretools Python library supports automated feature engineering and is published as open source.
Open source
24 alternatives
Feature Engine provides Python tools for feature engineering and selection that are compatible with scikit-learn.
Open source
24 alternatives
tsfresh automatically extracts relevant features from time series data.
Open source
24 alternativesPopular in AI Data & Analytics
Well-known listings from the same topic.
Dotgetdot.aiForvioforvio.comFluidAI Medicalfluidai.mdApache Zeppelinzeppelin.apache.orgSola Securitysola.securityQuadraticquadratichq.com
mljar-supervised FAQ
What data does mljar-supervised work with?
It works with tabular data.
What are the built-in AutoML modes?
Explain for understanding data, Perform for production pipelines, Compete for ensembling and stacking, and Optuna for tuning when computation time is not limited.
How is a web app created from a trained model?
After fitting, call automl.app() to generate files, automl.local_app() to run locally, or automl.publish_app() to publish.
Is something wrong or out of date?
Tell us and an editor will check it.
Own this website?
Claim this listing
Claim the listing and suggest updates yourself. An editor checks every claim first.