In the previous chapter, we talked about different best practices, approaches, and ways to boost code performance. As a toy example for performance, we'll build our own KNN model, which we used in Chapter 13, Training a Machine Learning Model. As a reminder, KNN is a simple ML model that predicts the target variable by identifying K closest records in the training set, then taking a mode (for classification) or weighted average (for regression) of the target variable. Obviously, there are quite a few implementations of KNN already, and so we will use one as an example.
For starters, let's write a naive implementation; it has already been fairly optimized through the use of NumPy commands. First, let's import all the Euclidean distance measuring functions and define a function to get the N-closest records. Take a look at the following...
