Feature Extraction
Data Science
The process of creating new variables from existing data to improve model performance.
Detailed Explanation
Feature engineering involves transforming raw data into meaningful features that enhance machine learning model accuracy. It includes techniques like creating new variables, encoding categorical data, and scaling features. Effective feature engineering helps models recognize patterns better, reduces complexity, and improves predictive performance, making it a vital step in the data science workflow.
Use Cases
- •Use case: Enhancing a credit scoring model by creating new financial ratio features from raw data for better prediction accuracy.
Related Terms
Other terms in the Data Science category