Welcome to miceforest’s documentation!

miceforest imputes missing data using LightGBM in an iterative method known as Multiple Imputation by Chained Equations (MICE). It was designed to be:

• Fast

  • Uses lightgbm as a backend

  • Has efficient mean matching solutions.

  • Can utilize GPU training

• Flexible

  • Can impute pandas dataframes and numpy arrays

  • Handles categorical data automatically

  • Fits into a sklearn pipeline

  • User can customize every aspect of the imputation process

• Production Ready

  • Can impute new, unseen datasets very quickly

  • Kernels are efficiently compressed during saving and loading

  • Data can be imputed in place to save memory

  • Can build models on non-missing data

There are very extensive beginner and advanced tutorials on the github readme. Below is a table of contents for the topics covered:

Contents:

• ImputationKernel
• ImputedData
• MeanMatchScheme
• utils