MissForest 2.2.1

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.

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.

How MissForest Handles Categorical Variables ?

Categorical variables in argument 'categoricals' will be label encoded for estimators to work properly.

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")

    # default estimators are lgbm classifier and regressor
    mf = MissForest()
    df_imputed = mf.fit_transform(df)

Imputing with other estimators

from missforest.missforest import MissForest
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier


if __name__ == "__main__":
    df = pd.read_csv("insurance.csv")
    df_or = df.copy()
    for c in df.columns:
        random_index = np.random.choice(df.index, size=100)
        df.loc[random_index, c] = np.nan

clf = RandomForestClassifier(n_jobs=-1)
rgr = RandomForestRegressor(n_jobs=-1)

mf = MissForest(clf, rgr)
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