What is PCA in machine learning and how do I interpret it?

What is PCA in machine learning and how do I interpret it?

PCA, or Principal Component Analysis, is a dimensionality reduction technique used to reduce the number of features in a dataset while preserving as much variance as possible. It transforms the data into a new coordinate system where the greatest variance comes to lie on the first coordinates (principal components). To interpret PCA, you look at the explained variance ratio to understand how much information each principal component retains.

How do I determine the number of principal components to keep?

You can use the explained variance ratio to determine the number of principal components to keep. A common approach is to plot the cumulative explained variance and choose the number of components that reach a threshold, like 90% or 95%.

Can you explain how to visualize PCA results?

Certainly! You can visualize PCA results using scatter plots. By plotting the first two or three principal components, you can see how the data points are distributed in the reduced space. Additionally, you can color the points based on different categories to observe how well-separated they are.

What are some common pitfalls when using PCA?

Some common pitfalls include over-reducing the dimensions, which can lead to loss of important information, and misinterpreting the principal components, as they are linear combinations of the original features and may not have a straightforward interpretation.