Hyper-parameter optimization method and device, computer equipment and storage medium

Abstract

The invention relates to a hyper-parameter optimization method and device, computer equipment and a storage medium. The method comprises the steps of acquiring a preset number of hyper-parameter setsand predicting the hyper-parameter set score of each hyper-parameter set through a trained empirical model; screening candidate hyper-parameter groups from the hyper-parameter groups according to thehyper-parameter group scores; according to the candidate hyper-parameter set and a preset machine learning operator, performing model training according to the training sample set corresponding to thetarget problem to obtain a trained target problem prediction model; testing the target problem prediction model according to the test sample set corresponding to the target problem to obtain an evaluation value corresponding to the candidate hyper-parameter group; updating a reference evaluation value currently corresponding to the target problem according to the evaluation value, and returning to the step of obtaining the preset number of hyper-parameter sets to continue to be executed until an iteration stop condition is met; and determining the candidate hyper-parameter set corresponding to the reference evaluation value as a target hyper-parameter set. The optimization efficiency can be improved.

Description

technical field [0001] The present application relates to the technical field of parameter optimization, in particular to a hyperparameter optimization method, device, computer equipment and storage medium. Background technique [0002] With the development of computer technology, machine learning technology has emerged, which can automatically mine valuable information from massive data. The premise of machine learning research and application is to improve the effect of machine learning algorithms, such as improving the effect of machine learning algorithms in terms of feature engineering, model selection, hyperparameter optimization, and result evaluation. Traditional machine learning relies on expert knowledge, which has problems of low efficiency and high cost. In order to solve this problem, automatic machine learning technology has gradually developed. Taking hyperparameter optimization as an example, the machine learning model is trained and evaluated based on the s...

AI Technical Summary

Problems solved by technology

In order to improve the optimization effect, the existing hyperparameter optimization algorithms usually require a large number of samples and evaluate them separately, which has the problem of low optimization efficiency

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