Lesson 7: K-Nearest Neighbors

Objectives

  • Understand the intuition behind the K-Nearest Neighbors (KNN) algorithm.
  • Learn how KNN makes predictions for classification tasks.
  • Understand the role of ‘K’ and how it affects the model.

Concept(s)

Today we’ll learn about a very intuitive algorithm called K-Nearest Neighbors (KNN). It’s a type of “lazy learning” algorithm, which means it doesn’t learn a “model” in the same way that other algorithms do. Instead, it just memorizes the entire training dataset.

How does KNN work?

The main idea behind KNN is that you can predict the class of a new data point by looking at the classes of its closest neighbors.

Imagine you’re in a new school and you don’t know anyone. You want to guess if a new student, Alex, plays sports. You could look at the 5 students who sit closest to Alex. If 4 of them are on the soccer team, you might guess that Alex also plays sports. That’s the basic idea of KNN!

Here’s how it works in a more formal way:

  1. Choose a value for K: K is the number of neighbors to look at. This is a number you choose.
  2. Find the K nearest neighbors: For a new data point, find the K data points in the training set that are closest to it. “Closeness” is usually measured using Euclidean distance (the straight-line distance between two points).
  3. Make a prediction: For classification, you take a “vote” among the K neighbors. The new data point is assigned to the class that is most common among its K neighbors.

Choosing the right K

The value of K is very important.

  • If K is too small, the model can be very sensitive to noise and outliers. A single mislabeled data point could change the prediction.
  • If K is too large, the model might become too “generalized” and miss the local patterns in the data.

Choosing the right value for K is a process of experimentation. You often try a few different values for K and see which one works best for your data.

Practice

Let’s see KNN in action. We’ll use it to classify different types of flowers based on their petal and sepal measurements.

We’ve created a Jupyter notebook that will guide you through building a KNN model. Click the link below to open it in Google Colab.

Open Lesson 7 Notebook in Colab

Summary

In this lesson, we learned about the K-Nearest Neighbors (KNN) algorithm, a simple and intuitive classifier. It works by assigning a new data point to the class that is most common among its ‘K’ closest neighbors in the training data. We also learned that choosing the right value for ‘K’ is a critical step in building an effective KNN model.