Description: Feature selection techniques are methods used to identify and select the most relevant features from a dataset that significantly contribute to the performance of a machine learning model. These techniques are fundamental in the data preprocessing process, as they help reduce the dimensionality of the feature space, which can improve model accuracy, reduce training time, and prevent overfitting. There are various feature selection techniques, which can be classified into three main categories: filtering methods, wrapper methods, and embedded methods. Filtering methods evaluate features independently of the model, using statistical metrics to select the most relevant ones. Wrapper methods, on the other hand, use a specific model to evaluate combinations of features and select those that enhance model performance. Finally, embedded methods integrate feature selection within the model training process, allowing the algorithm to learn which features are most important. Proper feature selection not only optimizes model performance but also facilitates the interpretation of results, which is crucial in applications where explainability is essential.
