Feature selection improves the efficiency of algorithms from "summary" of Machine Learning by Ethem Alpaydin
Feature selection is a crucial step in machine learning that can significantly improve the efficiency of algorithms. By selecting only the most relevant features from the data, the algorithm can focus on the most important information and ignore irrelevant or redundant features. This leads to faster training times and improved performance on unseen data. One of the main benefits of feature selection is reducing the dimensionality of the data. High-dimensional data can be computationally expensive to work with and may lead to overfitting. By selecting only the most informative features, the algorithm can generalize better to new data and avoid the curse of dimensionality. In addition to improvin...
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