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Successful machine learning depends on inputting features, or variables, that provide information that is useful for solving the problem at hand. Supervised classification, where the goal is to label or categorize new data based on patterns identified in labeled training data, is the most commonly applied task in machine learning. For these problems, the features used to train a machine learning model must help discriminate between different categories of data samples. However, it is not always possible to know a priori which of the available features are informative, and which are not. The presence of uninformative features can contribute noise and reduce the robustness and performance of classification models. Therefore, an important step in machine learning is the selection of informative features and the omission of uninformative features.

LFSpy: A Python Implementation of Local Feature Selection for Data Classification with scikit-learn Compatibility