---
title: "Imputers"
url: https://docs.catalyst.zoho.com/en/quickml/help/operations-in-quickml/imputers/
description: "QuickML is a fully no-code ML pipeline builder service in the Catalyst development platform for creating machine-learning pipelines with end-to-end solutions."
last_updated: 2023-10-08T05:18:45Z
service: QuickML
type: general
---

# Imputers
Imputation is a technique used for replacing the missing data in a dataset with some substitute value to retain most of the data/information of the dataset.

1. ### KNN Imputation
    This imputation utilises the k-Nearest neighbours method to replace the missing values in the datasets with the mean value from the parameter `n_neighbors` nearest neighbours found in the training set. By default, the `n_neigbours` parameter will be set to 5 and the Euclidean distance metric will be used to find the k nearest neighbours.
2. ### MissForest Imputation
    It initially imputes all missing data using the mean/mode. Then for each variable with missing values, a random forest model will be trained on the observed part and then predicts the missing part. 
3. ### Mean Imputation
    Mean Imputation replaces the null values with the mean of that feature
    across the whole dataset.
4. ### Median Imputation
    Median Imputation replaces the null values with the median of that feature
    across the whole dataset.
5. ### Mode Imputation
    Mode Imputation replaces the null values with the mode of that feature
    across the whole dataset. 
6. ### Group-By Imputation
    Group-by imputation takes the following 3 inputs for replacing the null values.
    * **Columns to Impute**: Columns that contains null values and that are needed to be replaced.
    * **Group-By Columns**: These columns will be grouped by while calculating values to replace null values.
    * **Aggregator**: Aggregate function like mean, median, minimum and maximum that need to be used.

    By using the above inputs, Group-By Imputation finds the required aggregate values by grouping the Group-By columns and fills the null values in the dataset with those aggregate values.