Data Science Feature Engineering and Selection 4

Question 1

Target leakage in feature engineering occurs when a feature:

Accepted Answer

Contains information not available at prediction time

Answer

Leakage happens when a feature reveals future or target information unavailable during real prediction.

Question 2

To avoid data leakage, feature scaling parameters should be fit on:

Accepted Answer

The training set only, then applied to the test set

Answer

Fitting scalers only on training data prevents test information from leaking into the pipeline.

Question 3

Mean imputation of missing values can distort which property of a feature?

Accepted Answer

Its variance, which becomes artificially reduced

Answer

Replacing missing values with the mean shrinks variance and weakens correlations.

Question 4

Creating a binary 'is_missing' indicator alongside imputation is useful because:

Accepted Answer

Missingness itself may be predictive

Answer

A missingness flag preserves the signal that a value was absent, which can be informative.

Question 5

From a timestamp, which is a typical engineered feature?

Accepted Answer

Day of week or hour of day

Answer

Decomposing timestamps into components like day-of-week or hour exposes cyclical patterns.

Data Science Practice Test