VISTA ACADEMY · MACHINE LEARNING

Lesson 20: Dimensionality Reduction

Explore PCA, explained variance, t-SNE, and UMAP through practical data-analysis scenarios.

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Introduction to Dimensionality Reduction

Dimensionality reduction represents data using fewer features or dimensions while trying to preserve information relevant to a task. It can help with visualization, noise reduction, and computational efficiency, but reducing dimensions may also discard useful information.

Why reduce dimensions?

A customer dataset may contain dozens of measurements. Plotting all features at once is difficult. A two-dimensional representation can help explore patterns, but the plot is only a simplified view of the original data.

BUSINESS ACTIVITY

A dataset has 80 features and is difficult to visualize. What can dimensionality reduction help with?

Create a lower-dimensional representation for explorationGuarantee that no information is lostProve that every feature is irrelevant

CONCEPT CHECK

What is a central goal of dimensionality reduction?

Represent data with fewer dimensions while retaining useful informationGuarantee every model becomes perfectly accurateAlways delete every correlated feature without checkingTurn every numeric feature into a category

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Why this matters at work

Dimensionality reduction helps analysts explore complex datasets, reduce redundant representations, and visualize structure. A clear plot is not proof of predictive quality or causality.

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