Iterative Imputation for Missing Values in Machine Learning


Datasets may have missing values, and this can cause problems for many machine learning algorithms.
As such, it is good practice to identify and replace missing values for each column in your input data prior to modeling your prediction task. This is called missing data imputation, or imputing for short.
A sophisticated approach involves defining a model to predict each missing feature as a function of all other features and to repeat this process of estimating feature values multiple times. The repetition allows the refined estimated values for other features to be used as input in subsequent iterations of predicting missing values. This is generally referred to as iterative imputation.
In this tutorial, you will discover how to use iterative imputation strategies for missing data in machine learning.
After completing this tutorial, you will know:

Missing values must be marked with NaN values and can be replaced with iteratively estimated values.
How to load a CSV value with missing values and mark the missing values with NaN values and report the number and percentage of missing values for each column.
How to impute missing values with iterative models as a data preparation method when evaluating models and when fitting a final model to make predictions on new data.

Let’s get started.

Iterative Imputation for Missing Values in Machine Learning Photo by Gergely Csatari , some rights reserved.

Tutorial Overview
This tutorial is divided into three parts; they are:

Iterative Imputation
Horse Colic Dataset
Iterative Imputation With IterativeImputer

IterativeImputer Data Transform
IterativeImputer and Model Evaluation
IterativeImputer and Different Imputation Order
IterativeImputer and Different Number of Iterations
IterativeImputer Transform When Making a Prediction

Iterative Imputation
A dataset may have missing values.
These are rows of data where one or more values or columns in that row are not present. The values may be missing completely or they may be marked with a special character or value, such as a question mark “?”.
Values could be missing for many reasons, often specific to the problem domain, and might include reasons such as corrupt measurements or unavailability.
Most machine learning algorithms require numeric input values, and a value to be present for each row and column in a dataset. As such, missing values can cause problems for machine learning algorithms.
As such, it is common to identify missing values in a dataset and replace them with a numeric value. This is called data imputing, or missing data imputation.
One approach to imputing missing values is to use an iterative imputation model .
Iterative imputation refers to a process where each feature is modeled as a function of the other...

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