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LogoutRFE is a wrapper-type feature selection algorithm. This means that a different machine learning algorithm is given and used in the core of the method, is wrapped by RFE, and used to help select features. This is in contrast to filter-based feature selections that score each feature and select those features with the largest (or smallest) score.
Technically, RFE is a wrapper-style feature selection algorithm that also uses filter-based feature selection internally.
RFE works by searching for a subset of features by starting with all features in the training dataset and successfully removing features until the desired number remains.
This is achieved by fitting the given machine learning algorithm used in the core of the model, ranking features by importance, discarding the least important features, and re-fitting the model. This process is repeated until a specified number of features remains