Original article was published by MRINAL WALIA on Artificial Intelligence on Medium
Missingpy is a library in python used for imputations of missing values. Currently, it supports K-Nearest Neighbours based imputation technique and MissForest i.e Random Forest-based imputation technique.
To install missingpy library, you can type the following in command line:
pip install missingpy
KNNImputer is a multivariate data imputation technique used for filling in the missing values using the K-Nearest Neighbours approach. Each missing value is filled by the mean value form the n nearest neighbours found in the training set, either weighted or unweighted.
If a sample has more than one feature missing then the neighbour for that sample can be different and if the number of neighbours is lesser than n_neighbour specified then there is no defined distance in the training set, the average of that training set is used during imputation.
Nearest neighbours are selected on the basis of distance metrics, by default it is set to euclidean distance and n_neighbour are specified to consider for each step.
It is another technique used to fill in the missing values using Random Forest in an iterated fashion. The candidate column is selected from the set of all the columns having the least number of missing values.
In the first step, all the other columns i.e non-candidate columns having missing values are filled with the mean for the numerical columns and mode for the categorical columns and after that imputer fits a random forest model with the candidate columns as the outcome variable(target variable) and remaining columns as independent variables and then filling the missing values in candidate column using the predictions from the fitted Random Forest model.
Then the imputer moves on and the next candidate column is selected with the second least number of missing values and the process repeats itself for each column with the missing values.