dimred_take_away.md

Main take-away

Wrap-up

In this module, we presented the framework used in unsupervised learning with dimensionality reduction, focusing on PCA and how to chose its number of components.

We explored the concepts of explained variance, reconstruction error and we saw how different the distribution of a given feature determines how it behaves after scaling, and the influence it has on the resulting PC space.

We showed how PCA can be integrated into both supervised and unsupervised pipelines to reduce computing time and to ease data visualization.

Finally, we introduced TSNE and UMAP as non-linear alternatives to PCA for visualization.

To go further

You can refer to the following scikit-learn examples which are related to the concepts approached during this module:

• Faces dataset decompositions
• Manifold learning on handwritten digits: locally linear embedding, Isomap
• Comparison of Manifold Learning methods
• Dimensionality Reduction with Neighborhood Components Analysis