How can KNN be used to handle missing data in datasets?

How can KNN be used to handle missing data in datasets?

KNN, or K-Nearest Neighbors, can be used to impute missing data by finding the nearest neighbors of a data point and using their values to fill in the gaps.

Can you explain how the KNN algorithm determines the nearest neighbors?

Sure! KNN calculates the distance between data points, typically using metrics like Euclidean distance, to identify the closest neighbors.

What are some advantages of using KNN for missing data imputation?

Some advantages include its simplicity, effectiveness in preserving local structure, and the ability to work with both numerical and categorical data.

Are there any limitations to using KNN for this purpose?

Yes, KNN can be sensitive to irrelevant features and may struggle with high-dimensional data, leading to increased computational costs.

What are some alternatives to KNN for handling missing data?

Alternatives include mean/mode imputation, regression imputation, and more advanced methods like multiple imputation or deep learning techniques.