👉 Scores engineering, also known as scoring or feature engineering, is a critical process in machine learning and data science where raw data is transformed into a format that better represents the underlying problem to the predictive models, improving their performance. This involves selecting, modifying, or creating new features from existing data to enhance model accuracy and interpretability. Common techniques include normalization, encoding categorical variables, creating interaction terms, and extracting meaningful patterns or summaries from time-series data. The goal is to highlight the most relevant information and reduce noise, enabling models to learn more effectively and make better predictions.
