What is PCA in data preprocessing?

What is PCA in data preprocessing?

PCA, or Principal Component Analysis, is a technique used to reduce the dimensionality of data while preserving as much variance as possible.

How does PCA help in improving model performance?

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

Can you explain how PCA works step by step?

Sure! PCA works by standardizing the data, calculating the covariance matrix, finding the eigenvalues and eigenvectors, and then projecting the data onto the new feature space defined by the top eigenvectors.

What are some limitations of PCA?

Some limitations include the assumption of linearity, sensitivity to outliers, and the fact that it may not always capture the most important features for classification tasks.