All 7 Dimensionality Reduction (PCA) questions
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What is Dimensionality Reduction (PCA) Why is Dimensionality Reduction (PCA) important in Supervised & Unsupervised Learning What are the key parts or types of Dimensionality Reduction (PCA) What are common mistakes to avoid with Dimensionality Reduction (PCA) What is a real world example of Dimensionality Reduction (PCA) What are best practices for Dimensionality Reduction (PCA)
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Supervised & Unsupervised Learning › Dimensionality Reduction (PCA)

How does Dimensionality Reduction (PCA) work

Beginner

Answer

PCA finds new combined features, called principal components, that capture the most variation in the data, allowing the dataset to be represented with fewer dimensions.
Real-world example A data team uses PCA to reduce a dataset with a hundred features down to ten while still keeping most of the useful information for their model.

Common follow-ups: What is Supervised & Unsupervised Learning, How is Dimensionality Reduction (PCA) evaluated in practice, What tools are commonly used for Dimensionality Reduction (PCA)

Supervised & Unsupervised Learning topics: Introduction to Supervised Learning Introduction to Unsupervised Learning Classification Algorithms
All 7 Dimensionality Reduction (PCA) questions