HyperImpute is a comprehensive library for handling missing data in your ML pipelines, simplifying the selection process of a data imputation algorithm and offering a range of novel algorithms compatible with sklearn.
How is it unique?
HyperImpute stands out with its fast, extensible dataset imputation algorithms, including the new iterative imputation method: HyperImpute. It provides classic methods like MICE, MissForest, GAIN, MIRACLE, MIWAE, Sinkhorn, SoftImpute, and more, all within a plugin-able architecture.
How is it useful?
HyperImpute can:
1. Streamline the handling of missing data in your ML pipelines, enabling faster and more reliable model training.
2. Improve the accuracy and quality of your data imputation, ultimately resulting in better model performance.
3. Offer a wide range of imputation algorithms, providing flexibility to choose the best-suited method for your specific needs.
4. Provide a sklearn-like interface, thus ensuring seamless integration with your existing machine learning workflows.
With HyperImpute, one can leverage state-of-the-art imputation methods to address the challenge of missing data, simplifying this routine yet crucial task, ultimately leading to more accurate and reliable models that drive innovation and improve patient outcomes.
