Feature Engineering
Feature engineering is really an umbrella term that generally refers to two main activities, feature extraction and feature selection. Effective feature engineering can significantly enhance model performance by providing algorithms with more informative inputs and reducing or removing “noisy” and/or uninformative ones.
Understanding Feature Engineering
Feature engineering encompasses two main activities:
- Creating New Features (Feature Extraction): This involves transforming existing data into new variables that may capture important patterns or relationships. For example, you might derive a "total spending" feature by combining "price" and "quantity" features.
- Selecting Relevant Features (Feature Selection): This process identifies and retains the most informative features while discarding those that do not contribute meaningfully to the model's predictive power.
Effective feature engineering can lead to simpler...
