How does PCA influence gradient-based optimization in ML models?

PCA can improve gradient descent convergence by reducing feature redundancy and conditioning.

Gradient-based models converge faster when input features are decorrelated and scaled. PCA improves conditioning of the feature space by removing redundancy and aligning data along orthogonal axes, which stabilizes gradient updates.