Data Science with Python Feature Engineering Techniques Flashcards
7 cards from real Data Science with Python Certification practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
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Which technique creates new features by computing the interaction between two existing numerical features?
Answer: Polynomial feature expansion
Polynomial feature expansion generates interaction terms and higher-degree powers of existing numerical features using sklearn's PolynomialFeatures.
What is the primary risk of applying target encoding before performing cross-validation?
Answer: Data leakage from the target variable
Applying target encoding before cross-validation leaks target information into the training folds, causing overly optimistic model evaluation.
When using pandas to extract the day-of-week from a datetime column, which method returns an integer (0=Monday, 6=Sunday)?
Answer: dt.weekday
The dt.weekday accessor returns an integer from 0 (Monday) to 6 (Sunday) for each datetime value in a pandas Series.
Which binning strategy creates intervals where each bin contains approximately the same number of observations?
Answer: Quantile-based binning
Quantile-based binning (e.g., pd.qcut) divides data so each bin holds roughly equal numbers of samples, handling skewed distributions well.
What does the 'handle_unknown' parameter control in sklearn's OneHotEncoder?
Answer: Behavior when an unseen category appears at transform time
handle_unknown='ignore' causes OneHotEncoder to output all-zero rows for unseen categories at transform time, preventing errors on new data.
Which feature selection method ranks features by computing a statistical test between each feature and the target, independently of any model?
Answer: Univariate feature selection
Univariate feature selection (e.g., sklearn's SelectKBest) scores each feature individually using a statistical test such as f_classif or chi2.
What is the purpose of applying a Box-Cox transformation to a numerical feature?
Answer: Stabilize variance and make the distribution more Gaussian
The Box-Cox transformation applies a power transformation to make a feature's distribution closer to normal and stabilize its variance across the range.
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