AI, Dimensionality Reduction, Machine Learning, Python, Data Science.
original features. 2. Assumption of Linearity: PCA assumes that the relationships between variables are linear, which may not be true in all cases. 3. Sensitive to Scaling: PCA is sensitive to the scale of the features, so standardization is often required. 4. Outliers Impact Results: Outliers can