m2cgen converts machine learning models into native code for multiple programming languages without runtime dependencies.
Open source
24 alternativesAuto ViML
Auto_ViML builds multiple machine learning models with a single line of code.
About Auto ViML
Auto ViML is an open-source Python library hosted on GitHub and created by Ram Seshadri. It is pronounced "auto vimal." The "V" stands for Variant because it tries multiple models, and the "i" stands for interpretable because it selects fewer features for simpler models.
You can pass in a dataframe as is and receive suggestions for missing values and formatting. It selects variables automatically, removes highly correlated features using the SULOV algorithm, and handles text, date-time, numeric, boolean, and categorical variables in one process. Setting verbose to 1 or 2 produces graphical performance results, and flags are available for GPU processing and imbalanced datasets.
It is built with scikit-learn, numpy, pandas, and matplotlib, and uses XGBoost, CatBoost, SHAP, and other libraries. Version 0.2 requires Python 3.12 or greater and pandas 2.0, including use in Colab and Kaggle kernels. It suits data scientists and Python users who work with tabular data and want to compare models.
Key features
- Build multiple ML models with a single line of code
- Automatic feature selection with SULOV correlation removal
- Handles text, date-time, numeric and categorical variables
- Graphical performance results with verbose settings
- Optional GPU processing and imbalanced-data handling
- Built on scikit-learn, pandas, XGBoost and CatBoost
Best for: Data scientists and Python users who want to compare multiple models and select features in notebooks such as Colab and Kaggle.
Good for
- Open-source Python code on GitHub
- Accepts dataframes as is for cleaning suggestions
- Combines cleaning, selection and modeling in one process
- Supports GPU and imbalanced datasets via flags
Watch out for
- Requires Python 3.12 or greater and pandas 2.0
- Requires extra libraries such as XGBoost and CatBoost
- GPU option needs correct runtime setting in Colab
Auto ViML at a glance
- 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
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Auto ViML FAQ
What is Auto ViML?
Auto ViML is an open-source Python library that builds multiple interpretable machine learning models with a single line of code.
What data types does Auto ViML handle?
It handles text, date-time, numeric, boolean, and categorical variables in one process, including lists and dictionaries.
What Python version does Auto ViML require?
Version 0.2 requires Python 3.12 or greater and pandas 2.0, including use in Colab and Kaggle kernels.
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