mljar builds a complete Machine Learning Pipeline + advanced feature engineering + algorithms selection and tuning + automatic documentation + ML explanations
There are four built-in modes in the mljar AutoML framework.
You can also create your own custom modes.
Perform exploratory analysis, search for a signal in the data, and discover relationships between features in your data with AutoML
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Train top ML models with advanced feature engineering, many algorithms, hyper-parameters tuning, Ensembling, and Stacking
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Stay a head of competitors and predict the future whith advanced ML. Deploy your models in the cloud or use them locally
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Highly tune ML models (LightGBM, CatBoost, Xgboost, Random Forest, Extra Trees) with Optuna framework and easy MLJAR AutoML API
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Don't need to worry about data conversions into numeric or missing values. AutoML will handle this for you!
Read More »Search for new features with great prediction power. Combine existing data into new Golden Features.
Read More »Ehance your data with new features created with K-Means clustering algorithm.
Read More »Use only relevant features. Automatically remove noisy features in your ML pipeline.
Read More »Discover your data properities with automatically created data visualizations.
Read More »Stop model training exactly when it is needed and avoid overfitting with early-stopping.
Understand your data and models with Machine Learning Explanations. Decision Tree plots, Features Importance, SHAP explanations, and more ...
Read More »Achieve great predictive accuracy with advanced ensembling techniques. Stack and combine models for performance improvements.
Read More »Track your model's metrics and results. Get full reproducibility for your Machine Learning experiments.
Improve your model performance by learning on previous model's errors. Boost sample weight values on difficult data points.
Results of the ML analysis are available as Markdown or HTML reports. You can check there: hyper-parameters, metrics, learning curves, plots, explanations.
Read More »The Markdown and HTML reports work perfect with Jupyter Notebooks, Kaggle Notebooks and GitHub. Present your results with elegance.
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The mljar provides the state-of-the-art performance on binary,
multiclass classfication, and regression.
Check the comparison of the popular AutoML frameworks on Kaggle datasets.
You can install MLJAR AutoML Python package
with PyPi repository, from source or run in the docker
container.
