Feature Engineering and Selection Flashcards
7 cards from real DSE practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Feature Engineering and Selection flashcards as text
Which technique creates new features by combining two or more existing features through multiplication or division?
Answer: Interaction terms
Interaction terms capture non-linear relationships by multiplying or dividing existing features to create new ones.
What is the primary purpose of applying a log transformation to a skewed feature?
Answer: To reduce the impact of outliers and make the distribution more symmetric
Log transformation compresses the range of large values, reducing skewness and the influence of extreme outliers.
In the context of feature selection, what does the term 'multicollinearity' refer to?
Answer: High correlation between two or more predictor features
Multicollinearity occurs when predictor features are highly correlated with each other, making it difficult to isolate individual effects.
Which encoding method is most appropriate for an ordinal categorical variable like 'education level' (High School < Bachelor's < Master's < PhD)?
Answer: Ordinal encoding
Ordinal encoding assigns integer values that preserve the natural order of the categories.
What is 'target leakage' in feature engineering?
Answer: Including features that contain information not available at prediction time
Target leakage occurs when features used during training contain information that would not be available when the model makes real predictions.
Which feature selection method uses a trained model's internal importance scores to rank and filter features?
Answer: Embedded method
Embedded methods integrate feature selection into the model training process, using coefficients or importance scores produced by the model itself.
When applying StandardScaler and then PCA for dimensionality reduction, why must StandardScaler be fit only on the training set?
Answer: To prevent information from the test set from influencing the transformation parameters
Fitting the scaler on the full dataset causes data leakage, as test set statistics influence the transformation applied during training.