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t-SNE Visualization with Yellowbrick — A Fast and Easy Method
t-SNE for visualizing high-dimensional data in a lower-dimensional space
In my previous article, I discussed how t-SNE can be used for visualizing high-dimensional data in a lower-dimensional space.
t-SNE is a non-linear dimensionality reduction technique that can be used to visualize high-dimensional data in a lower-dimensional space to find important clusters or groups in the data.
Today, we’ll implement the same type of visualization as we did in my previous article, but this time with Yellowbrick — A Python open-source package that can be used to create complex machine-learning visualizations with less code and time!
Prerequisites (Must read)
- How t-SNE Outperforms PCA in Dimensionality Reduction
- 10 Amazing Machine Learning Visualizations You Should Know in 2023
In Yellowbrick, t-SNE visualizations can be done using its Manifoldvisualizer (an object that learns from data to produce a visualization).
In machine learning, the term manifold refers to unsupervised approaches to non-linear dimensionality reduction which can be used to capture non-linear patterns by visualizing high-dimensional data in…
