KNNs & K-Means: The Superior Alternative to Clustering & Classification.

Author(s): Surya Maddula

Originally published on Towards AI.

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Let’s discuss two popular ML algorithms, KNNs and K-Means. Stick around; I’ll make this densely packed.

P.S. I’m trying out a new thing: I draw illustrations of graphs, etc., myself, so we’ll also look at some nice illustrations that help us understand the concept.

We will discuss KNNs, also known as K-Nearest Neighbours and K-Means Clustering. They are both ML Algorithms, and we’ll explore them more in detail in a bit.

K-Nearest Neighbors (KNN) is a supervised ML algorithm for classification and regression.

Principle: That similar data points are located close to each other in the feature space.

Quick Primer: What is Supervised? 💡"supervised" refers to a type of learning where the algorithm is trained using labeled data. This means that the input data comes with corresponding output labels that the model learns to predict.

So, KNNs is a supervised ML algorithm that we use for Classification and Regression, two types of supervised learning in ML. Let’s take a closer look at them:

The blue dots represent individual data points, each corresponding to a pair of input (x-axis) and output (y-axis) values.

The black line running through the data points is the regression line, which represents the… Read the full blog for free on Medium.

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Published via Towards AI