Data Science FREE Data Science Feature Engineering and Selection Questions and Answers 3

Question 1

Which dimensionality reduction technique is best suited for preserving local neighborhood structure in high-dimensional data?

Accepted Answer

t-SNE

Answer

t-SNE preserves local pairwise distances, making it effective for visualizing clusters in high-dimensional data.

Question 2

What is the purpose of applying a Box-Cox transformation to a feature?

Accepted Answer

To make the feature distribution more closely approximate a normal distribution

Answer

Box-Cox transformation applies a power function to stabilize variance and make data more Gaussian-like.

Question 3

When using L1 regularization (Lasso) for feature selection, what happens to irrelevant feature coefficients?

Accepted Answer

They are driven exactly to zero

Answer

L1 regularization penalizes the absolute value of coefficients, forcing unimportant ones to exactly zero.

Question 4

What is feature hashing (the hashing trick) primarily used for?

Accepted Answer

Reducing high-cardinality categorical features to a fixed-size vector

Answer

Feature hashing maps categorical values to a fixed number of buckets using a hash function, handling high cardinality efficiently.

Question 5

Which metric is commonly used in filter-based feature selection to rank features for a classification task?

Accepted Answer

Chi-squared statistic

Answer

The chi-squared test measures the dependence between a categorical feature and the target class, making it ideal for filter-based ranking.

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