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Dimensionality Reduction

Overview

Teaching: 0 min
Exercises: 0 min
Questions
  • How can we perform unsupervised learning with dimensionality reduction techniques such as PCA and TSNE?

Objectives
  • Recall that most data is inherently multidimensional

  • Understand that reducing the number of dimensions can simplify modelling and allow classifications to be performed.

  • Recall that PCA is a popular technique for dimensionality reduction.

  • Recall that TSNE is another technique for dimensionality reduction.

  • Apply PCA and TSNE with Scikit Learn to an example dataset.

  • Evaluate the relative peformance of PCA and TSNE.

Key Points

  • PCA is a dimensionality reduction technique

  • TSNE is another dimensionality reduction technique