K-Nearest Neighbors, or KNN, is a simple yet powerful machine learning algorithm used for classification and regression tasks. It falls under the category of supervised learning, where the algorithm learns from labeled data to make predictions about unseen or future data. KNN is intuitive and easy to understand, making it an excellent starting point for anyone delving into the world of machine learning.
In this article, we’ll explore KNN classification and create a Streamlit app to visualize how KNN works with a synthetic dataset. By the end of this article, you’ll have a solid grasp of KNN’s mechanics, including how to fine-tune the critical hyperparameter — the number of neighbors (k).
At the heart of the KNN algorithm is the idea that objects are more likely to be similar if they are close to each other. In the context of classification, KNN determines the class of a data point by considering the classes of its nearest neighbors. Let’s break down the steps:
- Data Collection: You start with a dataset that includes labeled examples. Each example has multiple features and belongs to a particular class.
- Choose the Number of Neighbors (k): KNN relies on a hyperparameter known as ‘k,’ which determines the number of neighbors to consider when making a prediction. Selecting an appropriate ‘k’ is crucial for the model’s performance.
- Calculate Distances: To find the nearest neighbors, the algorithm calculates the distance between the data point you want to classify and every other point in the dataset. Common distance metrics include Euclidean distance and Manhattan distance.
- Identify Neighbors: The ‘k’ data points with the smallest distances become the data point’s nearest neighbors.
- Majority Vote: KNN uses a majority vote system to classify the data point. It assigns the class that appears most frequently among the ‘k’ neighbors.
Now, let’s dive into creating a Streamlit app that demonstrates KNN classification. We’ll use a synthetic dataset with added noise…