What are the benefits of PCA in machine learning?

What are the benefits of using PCA in machine learning?

PCA, or Principal Component Analysis, helps reduce dimensionality, improving model performance and interpretability.

How does PCA improve model performance?

By reducing the number of features, PCA minimizes overfitting and speeds up training, leading to better generalization.

Are there any downsides to using PCA?

Yes, PCA can lead to loss of information and may not perform well if the data is not linearly separable.

Can PCA be used for data visualization?

Absolutely! PCA is often used to visualize high-dimensional data in 2D or 3D, making patterns easier to identify.