K-nearest neighbors classifier

Our first learning system will be the k-nearest neighbors (kNN) classifier. I will describe how the classifier makes predictions, the important hyperparameters it uses, and the problems that are faced by the classifier. Throughout, I will be using the classifier to predict species of iris flowers. So, let's go ahead and start a Jupyter Notebook for this classifier:

1. The first thing we're going to do is load in the dataset and other required functions, as follows:

The iris dataset is provided with sklearn. It is one of their example datasets, and is well known.

2. Then, we will load in an object that contains the iris data and save that into Python objects:

3. Then, we will divide the dataset into training and test data by using the following lines of code:

Here are the first five rows of the training data:

Here are the first five labels...