DSE Feature Engineering and Selection 4

Question 1

What is the main advantage of using quantile transformation over standard normalization for highly skewed data?

Accepted Answer

It maps values to a uniform or normal distribution, making it robust to outliers

Answer

Quantile transformation maps data to a target distribution based on rank, making it robust to extreme outliers unlike mean/std-based scaling.

Question 2

In feature engineering for NLP, what does TF-IDF stand for and what does it measure?

Accepted Answer

Term Frequency–Inverse Document Frequency; measures how important a word is to a document relative to a corpus

Answer

TF-IDF weights a term by how often it appears in a document (TF) discounted by how common it is across all documents (IDF), highlighting distinctive words.

Question 3

When would you prefer forward feature selection over backward feature elimination?

Accepted Answer

When the number of features is large relative to samples, making fitting a full model impractical

Answer

Forward selection starts with no features and adds one at a time, avoiding the need to fit a model on the full high-dimensional feature set.

Question 4

What is 'Weight of Evidence' (WoE) encoding primarily used for?

Accepted Answer

Encoding categorical features in binary classification, particularly in credit scoring models

Answer

WoE encodes each category as the log ratio of the proportion of events to non-events, making it well-suited for logistic regression in credit risk models.

Question 5

What problem does 'feature hashing' (the hashing trick) solve in machine learning pipelines?

Accepted Answer

Handles high-cardinality or unknown categorical values with a fixed-size feature vector

Answer

Feature hashing maps categories to a fixed-size vector using a hash function, allowing the model to handle unseen categories and large vocabularies without storing a full mapping.

DSE - Data Science Practice Test

DSE - Data Science Practice Test

DSE Feature Engineering and Selection 4