All 7 Dimensionality Reduction (PCA) questions
More in Dimensionality Reduction (PCA)
Why is Dimensionality Reduction (PCA) important in Supervised & Unsupervised Learning How does Dimensionality Reduction (PCA) work 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)
Advertisement
Back to results for “Classification Algorithms”
Supervised & Unsupervised Learning › Dimensionality Reduction (PCA)

What is Dimensionality Reduction (PCA)

Beginner

Answer

Dimensionality reduction, such as principal component analysis, is a technique that reduces the number of features in a dataset while keeping as much important information as possible.
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