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

One-hot encoding all unique values

Answer

Treating every category as an independent binary feature

Answer

Applying PCA to the raw strings

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 feature that measures processing delay in the pipeline

Answer

A feature derived from the frequency spectrum of the time series

Answer

A feature that encodes the time zone of each observation

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

Computational overhead increases linearly with features; motivates GPU usage

Answer

Memory limitations prevent storing high-dimensional arrays; motivates data compression

Answer

Gradient descent diverges in high-dimensional spaces; motivates learning rate tuning

Question 4

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

Accepted Answer

Embedded method

Answer

Filter method

Answer

Wrapper method

Answer

Projection method

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

Label encoding the hour values 0–23

Answer

Min-max scaling the hour to [0, 1]

Answer

Converting hour to a categorical ordinal variable

Question 6

When using Recursive Feature Elimination (RFE), how does the algorithm select features?

Accepted Answer

It trains a model on all features and removes the least important feature iteratively until the desired number remains

Answer

It ranks features by correlation with the target and selects the top-k

Answer

It projects features onto orthogonal components and selects those with highest variance

Answer

It tests every possible subset of features and chooses the subset with best cross-validation score

Question 7

What is the main advantage of using Polynomial Features in scikit-learn for feature engineering?

Accepted Answer

It generates interaction terms and higher-degree features to capture non-linear relationships

Answer

It reduces the feature space by combining redundant variables

Answer

It applies a kernel function to project data into infinite-dimensional space

Answer

It automatically selects the optimal polynomial degree via cross-validation