Recently I am dealing a classification problem with some algorithms, say logistic regression.
When I preprocess my data, I standardize all my features and then generate polynomial features based on them.
from sklearn.preprocessing import PolynomialFeatures, StandardScaler
and I do
# features is my entire features dataset, labels excluded features = poly.fit_transform(features) features = std.fit_transform(features)
After finishing training my model, the accuracy is, say about 80%. Then I invert the two line of preprocessing code to
features = std.fit_transform(features) features = poly.fit_transform(features)
I have read this post but it seems the answers is not strong enough to help me figure it out.
Should I standardize my data first or generate polynomials from original features first?