👉 Attributes engineering is a crucial process in machine learning and data science that involves the creation, selection, and transformation of features (attributes) from raw data to enhance model performance. It encompasses tasks such as feature extraction, feature selection, and feature construction, aiming to identify the most relevant and informative attributes that contribute to predictive accuracy. By optimizing these attributes, engineers can reduce dimensionality, improve model interpretability, and mitigate overfitting, ultimately leading to more robust and efficient machine learning models.
