baumhaus.digital/Art, Cognition, Education/Human and Machine Learning/Supervised learning/Features/Features (machine learning)
Features represent the input variables used by a machine learning model to make predictions or classifications. They are the building blocks of the dataset and provide the information necessary for the model to learn relationships and patterns. Features can be:
Numerical: Continuous or discrete values (e.g., height, number of words).
Categorical: Representing distinct groups (e.g., color, category labels).
Derived: Transformed or engineered values combining raw data (e.g., ratios, log values).
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