Tag: Principal Component Analysis (PCA) versus Feature Crossing

Feature crossing and Principal Component Analysis (PCA) are both techniques used in machine learning to manipulate features, but they serve different purposes and operate differently. Feature Crossing Feature crossing involves creating new features by combining existing ones. This technique is particularly useful in linear models, where interactions between features can help the model capture more…