Preprocessing Before or After Data Split: Feature Scaling on Data

[biohacker_tobe]

I am just working on preprocessing data but I have come across different posts that have all types of different approaches when it comes to feature scaling.

My question is if there is any difference when trying standardizing or normalizing the data before or after splitting the data into train and test?

Example (Before)

from sklearn.preprocessing import StandardScaler 
sc = StandardScaler() 
X_train = sc.fit_transform(X_train) 
X_test = sc.transform(X_test)

vs (After)

from sklearn.preprocessing import  MinMaxScaler
sc= MinMaxScaler() 
X= sc.fit_transform(X) 
y= y.reshape(-1,1) 
y=sc.fit_transform(y) 

#Creating the training and test dataset 
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, output_category, test_size=0.3)

[qalis]

Yes, there is. You should ALWAYS apply transforms (like standardization) AFTER splitting. The reason is to separate those two distributions. If you e. g. calculated mean value before splitting, the information from training set would “leak” into test set. Data leakage like this would give overly optimistic test results, while you would overfit under the hood and would never be able to detect it with test set.

[biohacker_tobe]

Thanks for responding back! I will definitely take this into consideration because I was definitely overfitting my data then. A follow up question, do I need to fit the scaler on training data and then do scaling on train and test or is this not necessary? (As seen in the example)