Data-driven modeling and machine learning share similarities but are not exactly the same thing.
Data-driven modeling refers to the process of building models directly from data, without explicitly specifying underlying relationships or mechanisms. These models are derived from observed data patterns and are used to make predictions, classifications, or decisions based on new data. Data-driven models can encompass a variety of techniques, including statistical methods, empirical models, and machine learning algorithms.
Machine learning is a subset of data-driven modeling that specifically focuses on algorithms and techniques that enable computers to learn from data and improve their performance over time without being explicitly programmed. Machine learning algorithms automatically identify patterns and relationships in data, which are then used to make predictions or decisions.
In summary, data-driven modeling is a broader concept that encompasses various approaches to building models from data, while machine learning is a specific subset of data-driven modeling that emphasizes the use of algorithms to enable computers to learn from data.