t-SNE is a very powerful machine learning algorithm that can be used to visualize a high-dimensional dataset also in two-dimensional figures. It stands for t-Distributed Stochastic Neighbor Embedding. If you want to learn more about t-SNE and how to visualize a high-dimensional dataset using t-SNE, this article is for you. In this article, I’ll give you an introduction to t-SNE in Machine Learning and its implementation using Python.
What is t-SNE?
One of the challenges a Data Scientist often faces is understanding the structure of a very complex dataset without visualizing it. This is where the t-Distributed Stochastic Neighbor Embedding algorithm comes in, it is used to visualize a high-dimensional dataset using a two-dimensional figure. You can also visualize a high-dimensional dataset using a three-dimensional figure, but the most important feature that it provides is that it can be used to reduce the dimensionality of the dataset to preserve internal relationships.
There are many visualization tools and libraries that you can use for implementing t-SNE using Python. In the section below, I’ll walk you through the implementation of t-SNE using Python to visualize a high-dimensional dataset on a two-dimensional figure using plotly.
t-SNE using Python
Now let’s see how to implement the t-Distributed Stochastic Neighbor Embedding algorithm in machine learning using the Python programming language. Here I will be using the classic iris dataset for this task. So here is how you can easily implement the t-SNE algorithm in machine learning using Python:
In the figure above, it is easily possible to see the iris species grouped according to their original distribution in the dataset. So this is how you can use the t-Distributed Stochastic Neighbor Embedding algorithm in machine learning to visualize a high-dimensional dataset in a short time.
Summary
So this is how you can implement the t-SNE algorithm in machine learning by using the Python programming language. It stands for t-Distributed Stochastic Neighbor Embedding and it is used for visualizing a high-dimensional dataset in a two-dimensional figure in a very short amount of time. I hope you liked this article on t-SNE in Machine Learning and its implementation using Python. Feel free to ask your valuable questions in the comments section below.
