IBM Data Science Practice Test 2025 – Comprehensive Exam Prep

The term "feature engineering" refers to the process of utilizing domain knowledge to create features from raw data that can effectively contribute to the predictive power of a machine learning model. It involves transforming raw data into a format that is better suited for analysis and improving model accuracy. This can include creating new variables, selecting relevant attributes, or modifying existing features to capture essential patterns and information.

Effective feature engineering often requires a deep understanding of the problem domain, as it allows data scientists to craft features that reflect the underlying mechanisms and provide relevant signals for the algorithms used in predictive modeling. This practice can lead to significant improvements in model performance by ensuring that the input data aligns more closely with the intended analysis or predictions.

In contrast, cleaning data, creating visualizations, and eliminating irrelevant features, while important tasks in the data science workflow, do not specifically describe the process of feature engineering. Cleaning data focuses on ensuring accuracy and consistency, visualizations aim to present data insights, and eliminating irrelevant features is part of feature selection rather than the broader scope of feature engineering itself.