Conclusion. Principal Component Analysis (PCA) is very useful to speed up the computation by reducing the dimensionality of the data. Plus, when you have high dimensionality with high correlated variable of one another, the PCA can improve the accuracy of classification model.

How does PCA improve performance in machine learning?

In machine learning, feature reduction is an essential preprocessing step. Therefore, PCA is an effective step of preprocessing for compression and noise removal in the data. It finds a new set of variables smaller than the original set of variables and thus reduces a dataset's dimensionality.

Why does PCA increase accuracy?

In theory the PCA makes no difference, but in practice it improves rate of training, simplifies the required neural structure to represent the data, and results in systems that better characterize the "intermediate structure" of the data instead of having to account for multiple scales - it is more accurate.