An imputer learns a numeric replacement value before the dataset is divided into training and test partitions. Why is this problematic?

1. The learned preprocessing can incorporate information from the held-out data
2. Numeric imputation is never permitted for machine learning
3. The test partition must always contain the target as an input
4. A larger test file always prevents leakage