👉 Indicator engineering is a sophisticated approach in machine learning and data science where new features or "indicators" are created to enhance the performance of predictive models. These engineered features are designed to capture underlying patterns, relationships, or domain-specific insights that may not be apparent from the raw data alone. By transforming existing variables or combining them in novel ways, indicator engineering aims to improve model interpretability, accuracy, and robustness. This process often involves domain expertise to identify meaningful transformations or interactions that can provide additional predictive power, making the model more effective in capturing complex data dynamics.
