100 Facts About Feature Engineering

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Here’s a list of 100 facts about Feature Engineering:

1. Feature engineering is the process of creating new features or modifying existing ones to improve the performance of machine learning models.
2. Well-engineered features can significantly enhance a model’s ability to learn patterns and make accurate predictions.
3. Feature engineering involves domain knowledge and creativity to extract meaningful information from raw data.
4. It can be more important than the choice of the model itself in some cases.
5. Feature engineering aims to highlight relevant information and reduce noise in the data.
6. It can involve scaling, normalization, or transformation of features to ensure consistency and better model performance.
7. Interaction features are created by combining two or more existing features to capture relationships that might be missed by the model.
8. Polynomial features involve creating new features based on polynomial combinations of existing features.
9. Logarithmic transformations are used to handle data with a skewed distribution.
10. Feature engineering is crucial in natural language processing (NLP) tasks to convert text data into a format suitable for modeling.