# What is T-SNE in Machine Learning?

T-SNE is a powerful machine learning algorithm that can be used to visualize high dimensional data. In this blog post, we’ll explain what T-SNE is and how it can be used to improve your machine learning models.

## What is T-SNE in Machine Learning?

T-SNE is an algorithm for reducing the dimensionalality of data. It is commonly used for visualizing high-dimensional data.

The idea behind T-SNE is to find a low-dimensional representation of the data that preserves the important relationships between the data points.

## What are the benefits of using T-SNE?

There are many benefits of using T-SNE in machine learning. T-SNE is a powerful tool that can help you visualise high-dimensional data, and it can also be used to improve the performance of your machine learning models. In addition, T-SNE is easy to use and it doesn’t require any prior knowledge of machine learning.

## How does T-SNE work?

T-SNE is a nonlinear dimensionality reduction technique that is particularly well suited for embedding high-dimensional data into a space of two or three dimensions, which can then be visualized in a scatter plot. The technique was developed by van der Maaten and Hinton in 2008, and has since been used extensively for data visualization and exploratory data analysis.

At a high level, T-SNE works by minimizing the divergence between two distributions: a distribution of points in the high-dimensional space, and a distribution of points in the low-dimensional space. The point of this optimization is to find a mapping from the high-dimensional space to the low-dimensional space that preserves as much of the structure of the original data as possible.

One way to think about this is to imagine that you are trying to place dots on a piece of paper such that the dots are spread out as evenly as possible, but such that they are also close together if they are similar in the original high-dimensional space. This results in a low-dimensional representation where points that are similar in the original space are close together, even if they are not close together in terms of any individual coordinate.

The optimization process starts with a random initialization (typically using a Gaussian distribution) and then proceeds iteratively until it converges on a locally optimal solution. In each iteration, T-SNE first computes pairwise affinities between all data points using some similarity metric (typically Euclidean distance), and then uses these affinities to compute gradient descent updates that modify the positions of the points in the low-dimensional space. The step size is automatically chosen such that early iterations make large updates (to allow Exploration) while later iterations make small updates (to allow Exploitation).

## When should T-SNE be used?

T-SNE is a powerful tool for visualizing data, but it is not always the best choice. If you have a large dataset with many features, T-SNE can be very slow. Also, because T-SNE is based on pairwise distances between data points, it can be sensitive to outliers. If your data has outliers, you may want to use another method.

## How to use T-SNE effectively?

T-SNE is a powerful tool for visualizing high-dimensional data, but it can be tricky to use effectively. This guide provides practical advice for getting the most out of T-SNE, including how to choose the right parameters, evaluate results, and troubleshoot common problems.

## T-SNE in action – some examples

T-SNE is a tool for visualizing high-dimensional data. It is particularly well suited for data with many points and few dimensions, such as images. T-SNE has been used to visualize handwritten digits, genomes, computer programs, and even conceptual ideas.

Here are some examples of T-SNE in action:

* Visualizing MNIST with T-SNE: http://www.columbia.edu/~jwp2128/TSNE/MNIST_tSNE.html
* T-SNE applied to proteins: http://www.nature.com/nbt/journal/v32/n11/full/nbt1464.html
* T-SNE applied to gene expression data: https://www.bioconductor.org/packages/release/bioc/vignettes/Rtsne/inst/doc/Rtsne_tutorial.html
* A T-SNE visualization of Wikipedia articles: http://tsneWikipediaR.readthedocs.io/#wikipedia

## T-SNE vs other dimensionality reduction techniques

T-SNE is a dimensionality reduction technique that is used to reduce the dimensionality of high-dimensional data while preserving local structure. It is well-suited for visualizing data with many features, and has been used extensively in the field of machine learning for exploratory data analysis and feature visualization.

T-SNE has a number of advantages over other dimensionality reduction techniques:

-It preserves local structure, which is important for visualizing data
-It is computationally efficient, making it feasible to use on large datasets
-It can be used with any distance metric, making it versatile

However, T-SNE also has some disadvantages:

-It can be sensitive to the parameters chosen, and requires careful tuning to produce good results
-It can be slower than other dimensionality reduction techniques

## Tips and tricks for using T-SNE

In machine learning, T-SNE is a technique for reducing the dimensionality of data. It is particularly useful for data that has many dimensions, such as images or text. T-SNE works by mapping the data to a lower-dimensional space in a way that preserves the local structure of the data.

There are a few things to keep in mind when using T-SNE:

-T-SNE is best suited for data that has many dimensions and complex structure.
-T-SNE can be sensitive to the parameters you choose, so it is important to experiment with different values to find what works best for your data.
-T-SNE can be slow for large datasets. You may need to use an approximate version of T-SNE if your dataset is very large.

– [Visualizing Data using t-SNE](https://towardsdatascience.com/visualising-high-dimensional-datasets-using-pca-and-t-sne-in-python-8ef87e7915b)