vanderschaarlab/hyperimpute
A framework for prototyping and benchmarking imputation methods
When preparing data for machine learning, you often encounter missing values that can hinder your analysis. HyperImpute helps you automatically select and apply the best method to fill in these gaps, taking raw datasets with missing information and producing complete datasets ready for your models. This is ideal for data scientists, machine learning engineers, and researchers who regularly work with real-world, imperfect data.
196 stars. No commits in the last 6 months.
Use this if you need to reliably handle missing data in your datasets and want to experiment with or automate the selection of various imputation techniques.
Not ideal if you prefer to manually implement imputation methods from scratch or need extremely fine-grained, manual control over every step of the imputation process.
Stars
196
Forks
17
Language
Python
License
MIT
Category
Last pushed
Apr 04, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/vanderschaarlab/hyperimpute"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
sktime/skpro
A unified framework for tabular probabilistic regression, time-to-event prediction, and...
WenjieDu/Awesome_Imputation
Awesome Deep Learning for Time-Series Imputation, including an unmissable paper and tool list...
WenjieDu/PyGrinder
PyGrinder: a Python toolkit for grinding data beans into the incomplete for real-world data...
ocbe-uio/imml
A Python package for integrating, processing, and analyzing incomplete multi-modal datasets.
DoubleML/doubleml-for-r
DoubleML - Double Machine Learning in R