👉 Feature computing is a process within machine learning where raw data is transformed into a set of features, or attributes, that capture the essential characteristics and patterns relevant to the task at hand. This transformation is crucial as it enables machine learning algorithms to better understand and make predictions based on the data. By extracting meaningful features, such as statistical measures or domain-specific indicators, feature computing simplifies complex data, reduces noise, and enhances the model's performance. These features serve as inputs to various algorithms, facilitating tasks like classification, regression, clustering, and more, ultimately aiding in accurate predictions or insights.