What is PCA and how do I interpret its results?

What is PCA and how do I interpret its results?

PCA, or Principal Component Analysis, is a statistical technique used to reduce the dimensionality of data while preserving as much variance as possible. It transforms the original variables into a new set of variables called principal components.

How do I know how many principal components to keep?

You can use the explained variance ratio to determine how many components to keep. A common approach is to look for the 'elbow' in a scree plot, where the explained variance starts to level off.

What does it mean if a principal component has a high loading for a variable?

A high loading indicates that the variable contributes significantly to that principal component. It means that the variable is important in explaining the variance captured by that component.

Can PCA be used for classification tasks?

Yes, PCA can be used as a preprocessing step for classification tasks. By reducing dimensionality, it can help improve the performance of classifiers by removing noise and redundant features.