DSE Feature Engineering and Selection 3

Question 1

What is the 'curse of dimensionality' and how does it affect feature engineering decisions?

Accepted Answer

Data becoming increasingly sparse as the number of features grows, degrading model performance

Answer

As dimensionality increases, data points become sparse and distance metrics lose meaning, motivating dimensionality reduction and careful feature selection.

Question 2

Which of the following is an advantage of mean encoding (target encoding) over one-hot encoding for high-cardinality categoricals?

Accepted Answer

It reduces the dimensionality increase caused by many unique categories

Answer

Mean encoding replaces each category with the mean target value, keeping the feature as a single column instead of creating hundreds of binary columns.

Question 3

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

Accepted Answer

A feature that uses a past observation's value as a predictor for the current time step

Answer

Lag features use values from previous time steps (e.g., sales yesterday) as predictors for the current time step.

Question 4

What does the Variance Inflation Factor (VIF) measure in feature selection?

Accepted Answer

The degree to which a feature's variance is explained by other features, indicating multicollinearity

Answer

VIF quantifies how much a feature's variance is inflated due to correlation with other features; VIF > 10 typically signals severe multicollinearity.

Question 5

What is 'feature crosses' as used in systems like TensorFlow Feature Columns?

Accepted Answer

Synthetic features created by crossing (combining) two or more categorical features

Answer

Feature crosses combine categorical features to create new synthetic features that capture interactions, such as crossing 'city' and 'day_of_week'.

DSE - Data Science Practice Test

DSE - Data Science Practice Test

DSE Feature Engineering and Selection 3