How can I handle missing data in anomaly detection?

How can I handle missing data in anomaly detection?

Handling missing data in anomaly detection can be approached in several ways. You can use imputation methods, remove missing values, or apply algorithms that can handle missing data directly.

What are some common imputation methods?

Common imputation methods include mean/mode/median imputation, k-nearest neighbors, and regression imputation.

Are there any algorithms specifically designed for missing data?

Yes, some algorithms like Random Forest and certain neural networks can handle missing data without requiring imputation.

What should I consider when choosing a method?

Consider the amount of missing data, the nature of your dataset, and the potential impact on your anomaly detection results.