5 Things you should know about TSNe

 Did you know that you can visualise data that has higher dimensions? We can do it using TSNe.

TSNe stands for t - distributed stochastic neighbour embedding



What is TSNe?

TSNe is a statistical method that is used to visualise high dimensional data by giving each datapoint a location on two or three dimensional map. It is a non linear dimensional reduction technique.

How TSNe works?

First it constructs a probability distribution over pairs of high dimensional objects. Similar objects are provided high probability and dissimilar objects are provided low probability. TSNe then defines a point on low dimensional map for similar objects.

When to use TSNe?

Usually with big data we have large number of columns or parameters associated with the data. Representing such data on two or three dimensional map is impossible. We cannot always go for deleting columns as it would lose all of its value then. In such cases we can use TSNe to repsent visually without losing much value.

How to use TSNe?

We can use TSNe by using sklearn library. Sklearn.manifold.TSNE . We can use it like following function:

TSNE(n_components = 2, learning_rate = ‘auto’, init=‘random’, perplexity=3).fit_tranform(X)

How to find out more about TSNe? 

You can learn more about TSNe from Sklearn website or on wikipedia page to know about it’s usage and why it’s being used in more details

If you also want to start your journey in data science, you can do by downloading your FREE copy of the roadmap to Data Science from the link below.

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