What are some applications of PCA in machine learning?

What are some applications of PCA in machine learning?

PCA, or Principal Component Analysis, is used for dimensionality reduction, data visualization, and noise reduction in datasets.

Can you give me an example of PCA in image processing?

Sure! PCA can be used to compress images by reducing the number of pixels while retaining essential features, making storage more efficient.

How does PCA help in improving model performance?

By reducing dimensionality, PCA helps eliminate noise and redundant features, which can lead to better model accuracy and faster training times.

Are there any limitations to using PCA?

Yes, PCA assumes linear relationships and may not perform well with non-linear data. It also can lose interpretability of features.