MS-DS Master of Data science Feature Engineering 2

Question 1

Which of the following is a valid technique to handle high-cardinality categorical features?

Accepted Answer

Target encoding with cross-validation

Answer

Target encoding with cross-validation handles high cardinality by summarizing each category with the mean target value while preventing data leakage through cross-validation folds.

Question 2

In time series feature engineering, what is a 'lag feature'?

Accepted Answer

A feature whose value is the observation from a prior time step

Answer

A lag feature captures the value of a variable at a previous time step, allowing models to learn temporal dependencies such as how past values influence current outcomes.

Question 3

What is the 'curse of dimensionality' and how does it motivate feature selection?

Accepted Answer

Data becomes increasingly sparse as dimensions grow, degrading model performance; motivates removing irrelevant features

Answer

As the number of features grows, the data becomes exponentially sparser in the feature space, making distance metrics unreliable and models prone to overfitting, which motivates removing irrelevant or redundant features.

Question 4

Which method uses a model's built-in feature importance scores to select features?

Accepted Answer

Embedded method

Answer

Embedded methods perform feature selection as part of the model training process itself (e.g., LASSO regression or tree-based feature importances), integrating selection and learning simultaneously.

Question 5

What transformation is typically applied to convert a cyclical feature such as 'hour of day' into usable numerical features?

Accepted Answer

Applying sine and cosine transformations based on the cycle length

Answer

Sine and cosine transformations map cyclical features onto a circle, ensuring that the model perceives hour 23 and hour 0 as close to each other rather than far apart.

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Feature Engineering 2