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

PCA

Answer

Forward selection

Answer

Lasso regression

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

To encode categorical variables as integers

Answer

To remove duplicate features from the dataset

Answer

To split a feature into multiple binary columns

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

They become very large positive values

Answer

They are replaced by the feature mean

Answer

They are converted to binary indicators

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

Encrypting sensitive feature values for privacy

Answer

Sorting features by their importance scores

Answer

Generating polynomial interaction terms

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

Mean squared error

Answer

R-squared

Answer

Silhouette score

Question 6

What problem does feature scaling solve when using K-Nearest Neighbors for prediction?

Accepted Answer

Prevents features with larger numeric ranges from dominating the distance calculation

Answer

Eliminates missing values from the dataset

Answer

Converts categorical features into numeric format

Answer

Reduces the total number of features in the model