kNNimputointi

kNNimputointi, or k-Nearest Neighbors Imputation, is a technique used in statistics and machine learning to estimate missing values in a dataset. It leverages the idea that data points that are close to each other in the feature space are likely to have similar values for their attributes.

The process begins by selecting a value for 'k', which represents the number of nearest neighbors to consider. For a data point with a missing value in a particular feature, the algorithm identifies the 'k' most similar data points in the dataset based on the available features. Similarity is typically measured using a distance metric, such as Euclidean distance.

Once the 'k' nearest neighbors are identified, the missing value for the target feature is imputed. If the feature is numerical, the imputed value is often the average or median of the values of that feature in the 'k' neighbors. If the feature is categorical, the imputed value is usually the most frequent category (mode) among the neighbors.

kNNimputointi is a non-parametric method, meaning it does not assume a specific underlying distribution for the data. It can be effective when the missingness mechanism is not well understood or when relationships between features are complex. However, its performance can be sensitive to the choice of 'k' and the distance metric used. It can also be computationally intensive for large datasets.