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Feature Selection

Feature Selection chooses a set of variables that will be present in the data records used for model training, testing and prediction processing. Feature selection is a crucial process in AI modeling that involves choosing the most relevant and informative features (variables or attributes) from a dataset to improve model performance, efficiency, and interpretability.

Factors

Factors to consider for feature selection include:

  • relevance: features should be correlated with the predictive objectives of the model

  • redundancy: redundancy of features should be minimized

  • dimensionality: manages the number of features

  • availability: features should be available in a sufficient number of model training records

  • accuracy: features should provide data that is as accurate as possible

Process

The feature selection process includes these elements:

References