Fully Explained K-Nearest Neighbors with Python

Haversine: used when there are radian values on input and output.
Hamming: It is used to calculate the equal length in the binary string.
Jaccard: It is based on the similarity point between two groups.

In this article, we will discuss the K-Nearest Neighbors classification problem with Python. The K-nearest neighbors (KNN) algorithm is a type of supervised machine learning algorithms. Machine learning classification algorithm study to solve real cases in data science.

Machine learning classification algorithm study to solve real cases in data science.

Hello Everyone, another article in the series fully explained machine learning algorithms. In this article, we will discuss the k nearest neighbor classification problem. A good article is like a flow of the story and readers get as much information in a small amount of time.

Let’s clarify some points

In the case of unsupervised nearest is a base for many learning techniques like clustering.
In the case of supervised learning, it comes in two categories: classification and regression.

So, we will discuss the supervised classification problem learning technique.

The main goal is to predict the new data point based on samples near that data point. These sample points can be based on k-value or radius-based. The k value is defined by the user and the radius is base on the density of data points.

To measure the distance between the new point and samples point near it is based on Euclidean distance or by Manhattan distance the former one is mostly used. Well, there are many distance metrics used to measure the distance like Chebyshev, Minkowski is in vector space with real values.

There are many other distance metrics also as shown below: