Model training method and device, electronic device and readable storage medium

Abstract

The embodiment of the invention discloses a model training method and device, an electronic device and a readable storage medium. The method comprises the steps of obtaining the first training data and the second training data, training a plurality of basic models based on the first training data, determining the model parameters of each basic model, and determining a basic model used in a combined model and the corresponding combination coefficients of the used basic model through a greedy algorithm based on the second training data. According to the technical scheme, the base model used in the combined model and the corresponding combination coefficient of the used base model can be automatically determined, the parameter adjusting efficiency in the model training process can be improved, and the accuracy and the objectivity of the model are improved.

Description

technical field [0001] The present disclosure relates to the field of computer technology, and in particular to a model training method, device, electronic equipment and readable storage medium. Background technique [0002] In order to improve the prediction accuracy of the model in machine learning, technicians usually combine multiple base models to improve the generalization ability of the model. [0003] In the process of proposing the present disclosure, the inventors found that the model combination in the prior art often requires technicians to train multiple base models separately, then select and combine the trained multiple base models, and combine the models Performing training to adjust the parameters of the model combination makes the existing model training time-consuming and laborious, which seriously affects the efficiency of model training. Contents of the invention [0004] In order to solve problems in related technologies, embodiments of the present d...

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Problems solved by technology

[0003] In the process of proposing the present disclosure, the inventors found that the model combination in the prior art often requires technicians to train multiple base models separately, then select and combine the trained multiple base models, and combine the models Performing training to adjust the parameters of the model combination makes the existing model training time-consuming and laborious, which seriously affects the efficiency of model training

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