AI, Dimensionality Reduction, Machine Learning, Python, Data Science.
significantly impact the results of PCA, as it focuses on capturing the maximum variance, which may be influenced by extreme values. When to Use: 1. High-Dimensional Data: PCA is particularly useful when dealing with datasets with a large number of features to mitigate the curse of dimensionality.