knn-k-Nearest Neighbors Algorithm Implementation for Binary Classification Function

In this section, we will elaborate on how the k-Nearest Neighbors (k-NN) algorithm is implemented in function code for binary classification. First, it is essential to understand the fundamental principle of the algorithm: comparing the test sample vector with known-class feature samples to identify the k nearest neighbors based on similarity, then determining the test sample's category through majority voting among these neighbors' classes. Key implementation considerations include selecting an optimal k-value using techniques like cross-validation and choosing appropriate distance metrics (e.g., Euclidean or Manhattan distance) for similarity measurement. For the input feature samples from two classes, we can perform preprocessing steps such as normalization to ensure comparable feature scales. The core function typically involves calculating distance matrices, sorting neighbors, and implementing voting logic. Ultimately, the function code outputs classification results, enabling effective data categorization and prediction with clear decision boundaries.