Add feature_clustering_selection method #209
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This is a correlation-based feature selection method. But unlike the already existing correlation_feature_selection that does not have a criteria to selected among correlated features, feature_clustering_selection first employs a feature clustering, using absolute correlation as distance metrics, following by the selection of the feature with lower 1-R2 metric from each cluster. 1-R2 metric allows to find the feature that most preserve the information (own cluster R2) from the other features from the same clusters, penalizing by the information (nearest cluster R2) present in the nearest cluster
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Background context
This is a correlation-based feature selection method. But unlike the already existing correlation_feature_selection which does not have a criteria to selected among correlated features, feature_clustering_selection first employs a feature clustering, using absolute correlation as distance metric, following by the selection of the feature with lower 1-R2 metric from each cluster. 1-R2 metric allows to find the feature that most preserve the information (own cluster R2) from the other features from the same clusters, penalizing by the information (nearest cluster R2) present in the nearest cluster.
Description of the changes proposed in the pull request
This commit will add the feature selection method feature_clustering_selection in fklearn/tuning/model_agnostic_fc.py
Where should the reviewer start?
The reviewer should start by method feature_clustering_selection at src/fklearn/tuning/model_agnostic_fc.py
The method test_feature_clustering_selection at fklearn/tests/tuning/test_model_agnostic_fc.py illustrates how is the method usage.