Description: Feature selection is a fundamental process in the field of machine learning and neural networks, which involves identifying and selecting a subset of relevant features for use in building predictive models. This process is crucial because the quality and relevance of features can significantly influence model performance. By reducing the dimensionality of the dataset, the risk of overfitting is minimized, model interpretability is improved, and training time is accelerated. Feature selection can be carried out using various techniques, including filtering, wrapper, and embedded methods. Filtering methods evaluate the relevance of features independently of the model, while wrapper methods use a specific model to assess the combination of features. On the other hand, embedded methods perform feature selection during the model training process. In the context of AutoML, feature selection is automated, allowing users to obtain optimized models without the need for deep technical knowledge. This democratizes access to artificial intelligence, facilitating its implementation across various industries and applications.
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