# Should I standardize first or generate polynomials first?

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?