Numerical features are attributes in a dataset that represent quantitative values and can be measured on a continuous or discrete scale. They play a crucial role in machine learning and data analysis, as they allow algorithms to perform mathematical calculations and statistical operations, contributing to model accuracy and performance. Understanding how to work with numerical features is essential for tasks like feature engineering and selection, where the goal is to enhance the predictive power of models by optimizing the use of these values.
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