Feature engineering: Difference between revisions

Features in machine learning refer to the attributes or characteristics of the data that can be used to describe or differentiate different [[class]]es or groups. Features are typically represented as columns in a [[dataset]], where each row corresponds to an [[example]] or [[data point]]. For example, in a dataset containing information about houses, features may include the number of bedrooms, the size of the living room, the age of the house, and the location of the house.  

Features are essential in machine learning, as they provide the basis for learning patterns and making predictions. However, not all features are equally important, and some may be irrelevant, redundant, or noisy, which can negatively impact the performance of machine learning models. Therefore, the process of feature engineering is crucial in identifying and selecting the most relevant and informative features for a particular problem.