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      Question

      What is 'feature engineering' in machine

      learning?
      A Engineering the hardware features of ML model training servers Correct Answer Incorrect Answer
      B Creating, transforming, or selecting features from raw data to improve model performance Correct Answer Incorrect Answer
      C Automatically selecting the best machine learning algorithm for a given dataset Correct Answer Incorrect Answer
      D The process of tuning hyperparameters of a model for optimal performance Correct Answer Incorrect Answer
      E Building the neural network architecture for deep learning models Correct Answer Incorrect Answer

      Solution

      Feature engineering often has more impact than algorithm choice. The techniques include Feature creation (debt-to-income ratio = total_debt/annual_income — more informative than raw values), Encoding (one-hot encoding for categorical variables like employment_type), Scaling (StandardScaler, MinMaxScaler — needed for distance-based algorithms), Binning (age groups), Log transformation (handling skewed income distributions), Date feature extraction (day_of_week, is_weekend for transaction fraud). In banking, creating features like "number of transactions in last 1 hour" or "velocity change vs 30-day average" are powerful fraud detection signals.

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