Final answer:
The KNN imputation method is a technique used to fill in missing values in a dataset by using the values from the k-nearest neighbors. It assumes that nearby data points are similar and uses their mean or median values for imputation.
Step-by-step explanation:
The KNN imputation method, or K-Nearest Neighbors imputation, is B) A method for imputing missing values based on nearby neighbors. In this method, the idea is that similar data points can be found in close proximity based on their feature distances. When a missing value is encountered, the KNN imputation algorithm looks for the k nearest neighbors among the data points that have a value for the attribute in question, and then imputes the missing value using the mean or median of these neighbors' values.
This is particularly useful in datasets where the missing data is 'Missing at Random' and there is a strong likelihood that the value can be accurately imputed based on those nearby data points that do not have missing values. However, this method assumes that data points close to each other are similar, which may not always be true, and that the choice of k can significantly influence the imputation quality.