What is imputation in machine learning?
Imputation is the process of replacing missing values in a dataset. It is a common technique to improve the performance of machine learning models. There are many imputation methods but the most common ones include:
Mean Imputation values: This involves taking the mean of the observed values and replacing the missing values with the derived mean.
Median Imputation: This involves replacing the missing values with the median of the observed values.
Mode Imputation: This involves replacing the missing value with the mode of the observed values.
KNN imputation: This involves replacing the missing values with the values of the k nearest neighbors.
Random forest imputation: This involves using a random forest model to predict the missing values.
The choice of imputation method depends on the data set and the machine learning model being used. We might think of the reasons why imputation has to be used in machine learning.
Imputation is used in machine learning for a few reasons:
To improve the accuracy of models: Missing values can lead to a bias in a machine learning model. In such a scenario, imputation can be used to improve the accuracy.
To make the data more consistent: Missing values can make data sets inconsistent, so imputation can be used to make them more consistent.
To make the data more complete: Missing values can make data sets incomplete, so imputation can be used to make them more complete.
To make the data more interpretable: Missing values can make data sets less interpretable, so imputation can be used to make them more interpretable.
I hope you found this blog post useful. In my next blog post, I will write more about how imputation affects the accuracy of machine learning models.