What is PCA in machine learning?

What is PCA in machine learning?

PCA, or Principal Component Analysis, is a dimensionality reduction technique used to reduce the number of features in a dataset.

How does PCA work?

PCA works by identifying the directions (principal components) in which the data varies the most and projecting the data onto these directions.

What are the benefits of using PCA?

The benefits of PCA include reducing computational cost, improving model performance, and helping to visualize high-dimensional data.

Are there any limitations to PCA?

Yes, PCA assumes linear relationships and may not perform well with non-linear data. It also can lose some information during reduction.

Can you give an example of PCA in Python?

Sure! You can use the sklearn library in Python to implement PCA. Would you like a code snippet?