Question

A data scientist is building a scikit-learn pipeline to preprocess a dataset before training a logistic regression model. The dataset contains numeric features with widely different ranges and several rows with missing values in those numeric columns. The scientist wants to (1) impute missing numeric values with the column median and (2) standardize the features. To avoid data leakage and produce correct results, in what order should these steps be arranged inside the pipeline, and how should they be fit?

Accepted Answer

Fit the median imputer first, then the StandardScaler, chaining both inside a single Pipeline that is fit only on the training data.

Answer

Fit the StandardScaler first on the full dataset, then impute missing values with the median so the scaler sees complete data.

Answer

Impute and scale the entire dataset before performing the train/test split so both sets share identical statistics.

Answer

Fit the imputer and scaler separately on both the training and test sets so each set is scaled to its own distribution.