Machine Learning Feature Engineering 3

Question 1

What is the purpose of creating 'interaction features' between two variables?

Accepted Answer

To capture combined effects that neither feature expresses alone

Answer

Interaction features represent the joint effect of two variables, enabling linear models to learn relationships that depend on both simultaneously.

Question 2

Which of the following best describes 'binning' (discretization) of a continuous feature?

Accepted Answer

Converting continuous values into discrete intervals or categories

Answer

Binning groups continuous values into buckets, which can help linear models capture non-linear patterns and reduce the effect of outliers.

Question 3

What does 'mean normalization' do to a feature?

Accepted Answer

Subtracts the mean and divides by the range (max - min)

Answer

Mean normalization centers the feature at zero and scales it by the range, resulting in values roughly between -1 and 1.

Question 4

What is the primary advantage of using 'embeddings' over one-hot encoding for high-cardinality categorical variables?

Accepted Answer

Embeddings produce dense, low-dimensional representations that capture semantic similarity

Answer

Embeddings learn a compact dense vector per category, capturing relationships between categories in a low-dimensional space unlike sparse one-hot vectors.

Question 5

Which feature selection method uses model coefficients or feature importances to rank features?

Accepted Answer

Embedded method

Answer

Embedded methods (like Lasso regularization or tree feature importances) perform feature selection as part of the model training process.

Machine Learning Practice Test

Machine Learning Practice Test

Machine Learning Feature Engineering 3