Enhancing KNN Performance: A Deep Dive into Hyperparameter Optimization

Hyperparameter Optimization in Machine Learning: Machine learning models, including KNN, come with various hyperparameters that need to be set before training the model. Examples of hyperparameters in KNN include the number of neighbors (K), distance metric, and weights assigned to neighbors. The choice of hyperparameter values can have a profound impact on the model's accuracy and generalization to new data. GridSearch: GridSearch is a systematic approach to hyperparameter optimization that involves defining a grid of hyperparameter values and exhaustively searching through all possible combinations. This method aims to find the optimal set of hyperparameters by evaluating the model's performance using a specified performance metric (e.g., accuracy, precision, or F1 score) for each combination in the grid.