Supervised & Unsupervised Learning › Dimensionality Reduction (PCA)

What are common mistakes to avoid with Dimensionality Reduction (PCA)

Intermediate

Answer

A common mistake with Dimensionality Reduction (PCA) is applying it without fully understanding the underlying data or problem, which often leads to weak or misleading results. Skipping proper testing before relying on it in a real project is another frequent error.

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