How does random forest typically compute feature importance?

By measuring the variance of the feature

By counting the number of missing values in the feature

By measuring the reduction in impurity (e.g., Gini impurity) when the feature is used for splitting

By calculating the correlation between the feature and the target

A data scientist is working with a dataset containing a 'FullName' column with values like 'John A. Smith'. The goal is to create features for a model that predicts customer lifetime value. Which feature engineering step is most appropriate?

Split the 'FullName' column into 'FirstName', 'MiddleName', and 'LastName'

Use the 'FullName' column as a categorical feature with one-hot encoding

Trim the 'FullName' column to remove whitespace

CertNexusCertified Data Science Practitioner (CDSP)

Domain 3Objective 3

Objective 3.3 Carry Out Feature Engineering CERTIFIED-DATA-SCIENCE-PRACTITIONER Practice Questions (Page 8)

Part of the 3.0 Performing exploratory data analysis domain, which accounts for 25-36% of the CERTIFIED-DATA-SCIENCE-PRACTITIONER exam.

54questions here

11free pages

27concepts

25-36%of the exam

27 Concepts in This Objective

One-hot encoding
Target encoding
Label/Ordinal encoding
Dummy encoding
Effect encoding
Binary encoding
Base-N encoding
Hash encoding
Split features
Text manipulation - Split
Text manipulation - Trim
Text manipulation - Reverse
Manipulate data - Split names
Manipulate data - Extract year from title
Convert dates to useful features
PCA (Principal Component Analysis)
Missing value ratio
t-SNE (t-Distributed Stochastic Neighbor Embedding)
Low-variance filter
Random forest feature importance
High-correlation filter
Backward feature elimination
SVD (Singular Value Decomposition)
Forward feature selection
False discovery rate
Factor analysis
Feature importance methods

Questions 36–40

36foundation · easy
How does random forest typically compute feature importance?
Select an answer first
37foundation · easy
A dataset has two numeric features that are highly correlated with each other. The data scientist wants to reduce redundancy by removing one of them. Which technique should they use?
Select an answer first
38foundation · easy
A categorical feature represents education level with values 'High School', 'Bachelor', 'Master', and 'PhD'. The data scientist wants to preserve the natural order of these levels. Which encoding method is most appropriate?
Select an answer first
39foundation · easy
A dataset has a feature where 80% of the values are missing. The data scientist wants to reduce noise by removing features with a high percentage of missing values. Which technique should they use?
Select an answer first
40application · easy
A data scientist is working with a dataset containing a 'FullName' column with values like 'John A. Smith'. The goal is to create features for a model that predicts customer lifetime value. Which feature engineering step is most appropriate?
Select an answer first

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Objective 3.3 Carry Out Feature Engineering CERTIFIED-DATA-SCIENCE-PRACTITIONER Practice Questions (Page 8)

Questions 36–40

How does random forest typically compute feature importance?

A dataset has two numeric features that are highly correlated with each other. The data scientist wants to reduce redundancy by removing one of them. Which technique should they use?

A categorical feature represents education level with values 'High School', 'Bachelor', 'Master', and 'PhD'. The data scientist wants to preserve the natural order of these levels. Which encoding method is most appropriate?

A dataset has a feature where 80% of the values are missing. The data scientist wants to reduce noise by removing features with a high percentage of missing values. Which technique should they use?

A data scientist is working with a dataset containing a 'FullName' column with values like 'John A. Smith'. The goal is to create features for a model that predicts customer lifetime value. Which feature engineering step is most appropriate?

Keep studying — 3.0 Performing exploratory data analysis