Question

A data scientist is preparing a dataset of customer features for a logistic regression model. Their preprocessing code applies a StandardScaler to the entire dataset first, and only afterward splits the data into training and test sets using train_test_split. During review, a colleague flags this as a methodological problem. What is the primary issue with this workflow ordering?

Accepted Answer

The scaler computes the mean and standard deviation using both training and test data, leaking test-set information into the training process and producing overly optimistic evaluation metrics.

Answer

StandardScaler cannot be applied before splitting because it requires the target column to be present, which is removed during the split.

Answer

Scaling before splitting causes the model to train more slowly because the scaled features increase memory usage across both partitions.

Answer

The test set will not be scaled at all, since train_test_split only preserves transformations applied to the training partition.