MissForest 2.0.0

Best imputation method.

missForest

This project is a Python implementation of the MissForest algorithm, a powerful tool designed to handle missing values in tabular datasets. The primary goal of this project is to provide users with a more accurate method of imputing missing data.

The project consists of a single function, fit_transform, which is responsible for imputing all missing values in a given dataset. While MissForest may take more time to process datasets compared to simpler imputation methods, it typically yields more accurate results.

Please note that the efficiency of MissForest is a trade-off for its accuracy. It is designed for those who prioritize data accuracy over processing speed. This makes it an excellent choice for projects where the quality of data is paramount.

Example

To install MissForest using pip.

pip install MissForest

Imputing a dataset:

from missforest.missforest import MissForest
import pandas as pd
import numpy as np


if __name__ == "__main__":
    df = pd.read_csv("insurance.csv")
    mf = MissForest()
    df_imputed = mf.fit_transform(df)

Benchmark

            Mean Absolute Percentage Error
           missForest | mean/mode | Difference
 charges        2.65%       9.72%       -7.07%
     age        1.16%       2.77%       -1.61%
     bmi        1.18%       1.25%       -0.07%
     sex        21.21       31.82       -10.61
  smoker         4.24        9.90        -5.66
  region        46.67       38.96        +7.71