Integrating GBDT Feature Derivation with Ensemble Learning for Customer Loyalty Prediction
In order to improve the accuracy of predicting bank customer loyalty,a customer loyalty prediction method combin-ing GBDT feature derivation and integrated learning is proposed.Firstly,the GBDT model is used to derive features from the origi-nal dataset to obtain a new dataset with more distinguishing features.And based on this,an SV-XLC model with integration learning as the core is proposed.The model is generally an improvement of Stacking.SV-XLC is divided into two parts,the primary predic-tion module and the secondary prediction module.The primary module consists of multiple base predictors,and the Voting model is embedded in the secondary module,which also consists of multiple base predictors.The dataset is first passed into the different base predictors of the primary module after a five-fold cross-validation.After processing,a new dataset is generated,which is substitut-ed into the different base predictors of Voting in the sub-module to train the prediction and get the final result.This paper is validat-ed on a public dataset of bank customers from Kaggle.The experimental results show that this method significantly improves the ac-curacy of bank customer loyalty prediction.
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