FREE Data Science Feature Engineering and Selection Questions and Answers

Question 1

A data scientist is preparing a dataset with a categorical feature "City" ('New York', 'London', 'Tokyo').
The feature has high cardinality and no inherent order.
Which encoding technique is most appropriate to convert this feature for a linear model without imposing a false ordinal relationship?

Accepted Answer

One-Hot Encoding

Answer

Label Encoding

Answer

Ordinal Encoding

Answer

Log Transformation

Question 2

Which category of feature selection methods evaluates feature subsets by training and testing a specific machine learning model on each subset?

Accepted Answer

Wrapper Methods

Answer

Filter Methods

Answer

Embedded Methods

Answer

Intrinsic Methods

Question 3

You are building a model using an algorithm that relies on distance calculations, such as K-Nearest Neighbors. Your dataset contains features with vastly different scales: 'Age' (18-90) and 'Annual_Income' (25,000-500,000). Which feature engineering step is most critical to ensure model performance is not biased by feature scale?

Accepted Answer

Applying standardization or normalization.

Answer

Using one-hot encoding on the 'Age' feature.

Answer

Creating interaction terms between 'Age' and 'Annual_Income'.

Answer

Performing mean imputation for any missing values.

Question 4

A data scientist is working with a retail dataset that includes 'price_per_item' and 'quantity_sold' for each transaction. To improve a sales prediction model, they create a new feature called 'total_transaction_value' by multiplying these two features. What is this process an example of?

Accepted Answer

Creating Interaction Features

Answer

Feature Binarization

Answer

Feature Discretization

Answer

Target Encoding

Question 5

When dealing with missing numerical data in a feature that is known to have a skewed distribution with significant outliers, which imputation method is generally the most robust?

Accepted Answer

Median Imputation

Answer

Mean Imputation

Answer

Mode Imputation

Answer

Constant Value Imputation (e.g., filling with zero)

Question 6

Which of the following is a key characteristic of embedded feature selection methods?

Accepted Answer

The feature selection process is an intrinsic part of the model training algorithm itself.

Answer

They are performed as a separate pre-processing step before any model is chosen.

Answer

They rely on statistical tests like Chi-Squared or ANOVA to rank features.

Answer

They are computationally the most expensive category of feature selection methods.

A data scientist is preparing a dataset with a categorical feature "City" ('New York', 'London', 'Tokyo').The feature has high cardinality and no inherent order.Which encoding technique is most appropriate to convert this feature for a linear model without imposing a false ordinal relationship?

A data scientist is preparing a dataset with a categorical feature "City" ('New York', 'London', 'Tokyo').
The feature has high cardinality and no inherent order.
Which encoding technique is most appropriate to convert this feature for a linear model without imposing a false ordinal relationship?