Machine Learning Feature Engineering 4

Question 1

Which of the following is an example of a 'date-derived' feature useful for forecasting retail sales?

Accepted Answer

Day of the week

Answer

Extracting the day of the week from a timestamp allows models to capture weekly seasonality patterns in sales data.

Question 2

What is 'recursive feature elimination' (RFE)?

Accepted Answer

Training a model and repeatedly removing the least important features until a target count is reached

Answer

RFE fits a model, ranks features by importance, removes the weakest feature(s), and repeats until the desired number of features remains.

Question 3

What does the Variance Inflation Factor (VIF) measure in the context of feature engineering?

Accepted Answer

The degree of multicollinearity between a feature and other features

Answer

A high VIF for a feature indicates it is highly correlated with other features, which can destabilize regression coefficient estimates.

Question 4

Which technique helps prevent data leakage when encoding categorical variables using cross-validation?

Accepted Answer

Using out-of-fold target encoding so each fold is encoded using only the other folds

Answer

Out-of-fold (cross-validated) encoding ensures that the target statistics used to encode each sample are computed from different samples, preventing leakage.

Question 5

In NLP feature engineering, what does TF-IDF downweight relative to raw term frequency?

Accepted Answer

Common words that appear across many documents

Answer

TF-IDF penalizes terms appearing in many documents (high document frequency) because they are less discriminative between documents.

Machine Learning Practice Test

Machine Learning Practice Test

Machine Learning Feature Engineering 4