Data Science with Python Certification Data Science with Python Feature Engineering Techniques 2

Question 1

Which technique creates new features by computing the interaction between two existing numerical features?

Accepted Answer

Polynomial feature expansion

Answer

Polynomial feature expansion generates interaction terms and higher-degree powers of existing numerical features using sklearn's PolynomialFeatures.

Question 2

What is the primary risk of applying target encoding before performing cross-validation?

Accepted Answer

Data leakage from the target variable

Answer

Applying target encoding before cross-validation leaks target information into the training folds, causing overly optimistic model evaluation.

Question 3

When using pandas to extract the day-of-week from a datetime column, which method returns an integer (0=Monday, 6=Sunday)?

Accepted Answer

dt.weekday

Answer

The dt.weekday accessor returns an integer from 0 (Monday) to 6 (Sunday) for each datetime value in a pandas Series.

Question 4

Which binning strategy creates intervals where each bin contains approximately the same number of observations?

Accepted Answer

Quantile-based binning

Answer

Quantile-based binning (e.g., pd.qcut) divides data so each bin holds roughly equal numbers of samples, handling skewed distributions well.

Question 5

What does the 'handle_unknown' parameter control in sklearn's OneHotEncoder?

Accepted Answer

Behavior when an unseen category appears at transform time

Answer

handle_unknown='ignore' causes OneHotEncoder to output all-zero rows for unseen categories at transform time, preventing errors on new data.

Data Science with Python Certification Practice Test