Machine Learning Feature Engineering 5

Question 1

What is 'feature agglomeration' in scikit-learn?

Accepted Answer

Merging similar features into groups using hierarchical clustering

Answer

FeatureAgglomeration applies hierarchical clustering to group correlated features into clusters and replaces each cluster with its centroid value.

Question 2

Which approach is most effective for handling cyclical features like 'hour of day' or 'month of year'?

Accepted Answer

Encoding them as sine and cosine components

Answer

Sine and cosine encoding preserves the cyclical nature so that hour 23 and hour 0 are close together in the encoded space.

Question 3

What is the 'curse of dimensionality' effect on distance-based models when many features are added?

Accepted Answer

Distances between points become increasingly similar, making nearest-neighbor methods unreliable

Answer

In high-dimensional spaces, pairwise distances concentrate around the same value, reducing the discriminative power of distance-based algorithms like k-NN.

Question 4

When applying PCA for dimensionality reduction before classification, on which dataset should the PCA be fit?

Accepted Answer

The training set only, then applied to the test set

Answer

Fitting PCA only on training data prevents test-set information from influencing the transformation, which would constitute data leakage.

Question 5

What is 'weight of evidence' (WoE) encoding most commonly used for?

Accepted Answer

Encoding categorical variables for binary classification, especially in credit scoring

Answer

WoE encodes categories based on the log ratio of the proportion of events to non-events, which is particularly useful in logistic regression for credit risk models.

Machine Learning Practice Test

Machine Learning Practice Test

Machine Learning Feature Engineering 5