Which features carry essentially the most weight? How do original features contribute to principal components? These 5 visualization types have the reply.
Principal Component Evaluation (PCA) can inform you quite a bit about your data. Briefly, it’s a dimensionality reduction technique used to bring high-dimensional datasets right into a space that could be visualized.
But I assume you already know that. If not, check my from-scratch guide.
Today, we only care in regards to the visuals. By the top of the article, you’ll know find out how to create and interpret:
- Explained variance plot
- Cumulative explained variance plot
- 2D/3D component scatter plot
- Attribute biplot
- Loading rating plot
I’d like to dive into visualizations immediately, but you’ll need data to follow along. This section covers data loading, preprocessing, PCA fitting, and general Matplotlib styling tweaks.
