How can one describe the process of discovering patterns through machine learning?

The process of discovering patterns through machine learning is inherently adaptive and data-driven. Machine learning algorithms analyze vast amounts of data, identifying trends, correlations, and anomalies without explicit human programming for each specific task. This adaptability allows them to improve their performance as more data becomes available, enabling the model to refine its predictions or classifications based on newly learned insights.

While human guidance can be involved in aspects such as model selection, feature engineering, and interpreting results, the core of machine learning relies on the ability to learn from data directly. This data-driven approach differentiates machine learning from traditional programming, where fixed rules and logic are predefined by humans.

In contrast, relying on a closed set of data would limit the model's ability to learn dynamically from varied inputs, while dependence on fixed rules would constrain its capacity for adaptation based on new information. Therefore, the adaptive, data-driven nature is crucial for effective pattern discovery in machine learning.