Which method involves creating interaction terms between variables to capture combined effects in a model?
- Principal Component Analysis (PCA)
- Feature Engineering
- Feature Scaling
- Hypothesis Testing
Feature Engineering involves creating interaction terms or combinations of variables to capture the combined effects of those variables in a predictive model. These engineered features can enhance the model's ability to capture complex relationships in the data. PCA is a dimensionality reduction technique, and the other options are not directly related to creating interaction terms.
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