Xgboost-pca-based take-out platform rating method

Abstract

The application relates to the technical field of network catering services, in particular to a catering takeout platform scoring method based on XGBoost-PCA, which comprises the following steps: constructing a modular scoring system; performing normalization processing on the data of each module; calculating the original module scores of each module based on an XGBoost algorithm; performing dimension reduction on the original module scores by adopting a PCA principal component analysis algorithm; and performing weighted summation on the module dimension reduction scores to obtain a final platform score; the application constructs multiple decision trees by XGBoost, calculates the gain of different features at a split node, identifies the index with the largest contribution to the prediction result and eliminates the redundant weak features, and further combines PCA to analyze the linear correlation between the features, eliminates the linear redundancy by maximizing the variance, and obtains the dimension reduction comprehensive score, which removes the repeated potential factors in the original data, avoids repeated calculation of these factors in subsequent evaluation, and thus ensures the objectivity of the evaluation.

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