How does PCA affect model training time and computational efficiency?

PCA reduces training time by lowering dimensionality and computational complexity of downstream models.

Many ML algorithms scale with number of features. By reducing feature space, PCA decreases computation cost for matrix operations, distance calculations, and gradient updates. However, PCA itself introduces preprocessing overhead.