In toxicological research, datasets often include various types of data, such as chemical properties, biological responses, and environmental factors. Missing data in these datasets can arise due to several reasons, including experimental errors, data collection issues, or incomplete research trials. k-NN imputation is particularly useful in toxicology because:
Flexibility: It can handle both numerical and categorical data, which are common in toxicological datasets. Preservation of Relationships: By using neighboring data points for imputation, k-NN maintains the inherent relationships and patterns within the dataset. Simplicity: The algorithm is easy to implement and understand, making it accessible for researchers without a strong background in data science.
