Hyperparameter Tuning in KNN

Hyperparameter tuning is a critical step in optimizing the performance of machine learning models, including KNN, but…what exactly is a hyperparameter anyway? Well, when we think about training an ML model, we can think of that model as a mathematical equation where we provide some input data and output data (in the case of supervised learning) and train our model to determine the best set of parameters in the equation to generally match our inputs to our outputs.

For example, if you remember the equation for a linear regression model, , the x is our input data, the y is our output data, and the m and b are the slope and y-intercept of our line, respectively. These values are the parameters learned during training. So, while parameters are something our model learns during training, hyperparameters, in essence, tell our model how to learn. In this recipe, we’ll learn how to apply two different search techniques for finding the optimal mode...