You’ve now written your own K-Nearest Neighbor classifier from scratch! However, rather than writing your own classifier every time, you can use Python’s sklearn library. sklearn is a Python library specifically used for Machine Learning. It has an amazing number of features, but for now, we’re only going to investigate its K-Nearest Neighbor classifier.

There are a couple of steps we’ll need to go through in order to use the library. First, you need to create a KNeighborsClassifier object. This object takes one parameter - k. For example, the code below will create a classifier where k = 3

classifier = KNeighborsClassifier(n_neighbors = 3)

Next, we’ll need to train our classifier. The .fit() method takes two parameters. The first is a list of points, and the second is the labels associated with those points. So for our movie example, we might have something like this

training_points = [
  [0.5, 0.2, 0.1],
  [0.9, 0.7, 0.3],
  [0.4, 0.5, 0.7]
]

training_labels = [0, 1, 1]
classifier.fit(training_points, training_labels)

Finally, after training the model, we can classify new points. The .predict() method takes a list of points that you want to classify. It returns a list of its guesses for those points.

unknown_points = [
  [0.2, 0.1, 0.7],
  [0.4, 0.7, 0.6],
  [0.5, 0.8, 0.1]
]

guesses = classifier.predict(unknown_points)