What is Feature Engineering?
Feature engineering is the process of selecting, modifying, or creating new features from raw data to help improve the performance of machine learning models. It is a crucial step in the data science workflow.
Importance of Feature Engineering
Proper feature engineering can make predictive models more accurate! It provides the following benefits:
- Improved accuracy: With relevant features, models can better capture the underlying patterns.
- Reduced complexity: Removing redundant or irrelevant features leads to simpler models that generalize better.
- Enhanced interpretability: Well-engineered features can provide insights into the data and the model's decision process.
Common Techniques
- Normalization and Standardization
- Handling Categorical Data
- Feature Extraction: Principal Component Analysis (PCA)
- Feature Selection: Recursive Feature Elimination (RFE)