Remember PCA
Full name: Principal Component Analysis
Memory aid: Principal Components capture the strongest variance Axes as a cue.
High variance does not necessarily mean high predictive usefulness.
Study area: AI & machine learning · Batch 9
Relevant study for: Azure AI Fundamentals AI-901 · Azure AI Apps and Agents Developer AI-103 · Azure AI Fundamentals (retired) AI-900 · Azure AI Engineer (retired) AI-102
Practice with exam flashcards →AI Models & Assistants / Knowledge library
PCA — Principal Component Analysis
Finds orthogonal directions capturing variance in numerical data.
Why learn PCA — Principal Component Analysis?
Finds orthogonal directions capturing variance in numerical data.
Where it came from
High variance does not necessarily mean high predictive usefulness.
- Memory aid (a learning cue, not the expansion): Principal Components capture the strongest variance Axes as a cue.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Without looking, expand PCA: Principal Component Analysis.
- Recall this memory aid: Principal Components capture the strongest variance Axes as a cue.
- Explain the practical use: Finds orthogonal directions capturing variance in numerical data.
- Explain this distinction: High variance does not necessarily mean high predictive usefulness.
- Identify PCA in a device specification, architecture diagram, or system inventory.
Documentation and sources
- scikit-learn machine learning user guide (scikit-learn.org)