Explain feature engineering strategies for Logistic Regression

Feature engineering improves Logistic Regression performance by transforming raw data into meaningful predictive signals.

Feature engineering is one of the most important aspects of successful Logistic Regression systems. Since Logistic Regression is fundamentally a linear model, the quality of input features directly determines model performance.

Raw data rarely contains perfectly separable patterns. Feature engineering helps the model capture hidden relationships, interactions, and domain-specific behavior.

Major feature engineering techniques include:

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