Birds of the Same Feather Flock Together: Explaining K Nearest Neighbors for Absolute Beginners

 Just as birds of the same feather flock together, data points in a dataset with similar characteristics tend to cluster together. This is the fundamental idea behind the K Nearest Neighbors (KNN) algorithm, a popular and intuitive method used in machine learning. This blog post aims to explain KNN in a way that is accessible and understandable for everyone.

What is K Nearest Neighbors?

K Nearest Neighbors, or KNN, is a supervised machine learning algorithm that classifies a data point based on how its neighbors are classified. To illustrate, imagine you're trying to understand a person's character. One way you might do this is by looking at their friends or the group they hang out with. If most of their friends are athletes, you might classify this person as an athlete too. This is similar to how KNN works.

However, KNN is considered a lazy learning algorithm, meaning it doesn't immediately generalize from the training data. It waits until it is given a test observation, then it goes through the entire training set to find the K most similar instances. It's like getting to know someone deeply before making a judgment, instead of just basing your opinion on their friends or group.

Finally, KNN makes no assumptions about the underlying data distribution. This is a fancy way of saying that KNN doesn't require the data to follow a specific structure or type. It's flexible and can work with different kinds of data. This feature of KNN is often referred to as being "non-parametric". In simpler terms, "non-parametric" means that KNN doesn't make strong assumptions about the form of the mapping function from inputs to outputs. It's like being open-minded and not having preconceived notions about someone based on their background or appearance.

How Does KNN Work?

Think of KNN as a detective solving a case. The detective (KNN) has a mystery person (the new, unknown data point) and needs to figure out which group this person belongs to. The detective uses clues (data points) and witnesses (neighbors) to solve the case. Here's how our detective goes about it:

Step 1: Choose the number K of neighbors (witnesses): The detective decides how many witnesses to question. This is the 'K' in KNN, and it's usually an odd number if there are 2 groups to choose from.

Step 2: Calculate the distance (gather clues): The detective measures how similar each witness's story is to the mystery person's story. This is like calculating the distance between data points, and it can be done in various ways, including Euclidean or Manhattan distances (No statistics background… Just Ignore).

Step 3: Find the nearest neighbors (identify key witnesses): The detective identifies the K witnesses whose stories are most similar to the mystery person's story.

Step 4: Vote for labels (make a decision): Based on the majority of the witnesses' stories, the detective decides which group the mystery person belongs to.

And that's how KNN works! It's a simple but powerful process, much like solving a mystery.

Choosing the Value of K

Choosing the right value for K is like deciding how many people to invite to a party. Invite too few, and you might miss out on the fun (in KNN, a small K means that noise will have a higher influence on the result). Invite too many, and the party becomes chaotic and hard to manage (in KNN, a large K is computationally expensive).

So, how do you decide the right number of guests (or the right K value)? Well, data scientists often choose an odd number if there are 2 groups to avoid a tie (imagine a vote with an even number of people - you could end up with a draw!).

A common practice is to invite guests equal to the square root of the total number of people you know (in KNN, we often choose K as the square root of the total number of data points). This way, you have a good mix of people at your party, just like having a balanced representation of data points in your KNN algorithm.

Implementing KNN with Python

Now, let's bring our detective story to life with some Python code! If you're new to Python, don't worry. You can think of this as a sneak peek into how our detective (the KNN algorithm) works behind the scenes. If you're not interested in coding, feel free to skip this part.

In this script, we're using Python's scikit-learn library, which has a built-in class for KNN. We're applying our KNN detective to a famous case, the Iris dataset. We split the dataset into a training set (the witnesses we question) and a test set (the mystery people we want to classify). We then train our detective on the training set and test its accuracy on the test set.

Remember, you don't need to understand every line of code to grasp the concept of KNN. Just like you don't need to know how a car engine works to drive a car, you don't need to know all the coding details to understand KNN. But if you're curious and want to learn more, there are plenty of resources available to help you dive deeper into Python and machine learning.

Conclusion

And there you have it, folks! Just like a vibrant parade during Sierra Leone's Independence Day, we've danced our way through the world of K Nearest Neighbors together. We've seen how this simple yet powerful algorithm can help us make sense of our world, one data point at a time.

Remember, learning is a journey, not a destination. So, don't worry if you didn't catch all Dj Tunji's beats at once. Keep exploring, keep asking questions, and keep learning. You're doing great!

Before we part ways, I have an exciting invitation for you:

Become a part of the STEAD Society Telegram Community. It's a space where we dive deeper into the world of Science, Technology, Engineering, and Arts.

By joining this community, you'll stay at the forefront of AI and machine learning, and be part of a group of lifelong learners.

As we say goodbye, remember to always stay curious and keep learning. The world of machine learning and artificial intelligence is vast and exciting, and we at The AI Corner can't wait to explore it with you. Until our next parade, keep dancing to the rhythm of learning!

Peace out!