Description: The training of the K-nearest neighbors (K-NN) model is a fundamental process in the field of machine learning and data mining. This method is based on the idea that similar instances tend to be close to each other in the feature space. During training, the model stores the features and labels of the training instances without performing explicit learning. When a new instance is presented, the model calculates the distance between it and the stored instances, selecting the ‘K’ nearest neighbors. Classification or regression is performed based on the majority of the neighbors’ labels or the average of their values, respectively. This approach is intuitive and easy to implement, making it a popular choice for various classification and regression tasks. However, its performance can be affected by the choice of the ‘K’ value, the distance metric used, and the dimensionality of the data. Therefore, hyperparameter optimization is crucial to improve the accuracy and efficiency of the K-NN model, allowing for the adjustment of these parameters to achieve the best results on diverse datasets.
