Machine learning hyper-parameter adjusting method and system

Abstract

The invention discloses a machine learning hyper-parameter adjusting method and system, relating to the technical field of machine learning. According to the invention, a Bayesian optimization framework is adopted, a prior probability model is generated by a Gaussian process through an initial observation set, a next evaluation point is searched by using a chaotic particle swarm method, a new evaluation point is evaluated to obtain an observation value corresponding to the new evaluation point, a Gaussian process probability agent model is updated by updating the observation set, and an optimal hyper-parameter combination is obtained through multiple iterative search updates. According to the method, the evaluation frequency of hyper-parameter tuning of a machine learning model can be effectively reduced, the defect that traditional Bayesian optimization falls into the local optimal point is overcome, and hyper-parameter tuning can be more accurately and efficiently carried out.

Description

【Technical field】 [0001] The invention relates to the technical field of machine learning, in particular to a method and system for adjusting machine learning hyperparameters. 【Background technique】 [0002] The performance of machine learning algorithms is highly dependent on the selection of hyperparameters, and the selection of hyperparameters for machine learning is a tedious but crucial task. The methods used in the prior art include manual parameter adjustment methods, automatic parameter adjustment methods such as grid search, random search, and Bayesian optimization methods. However, there are various problems in these methods: the manual parameter tuning method has a strong dependence on experience; the automatic parameter tuning method is less efficient because it wastes time to evaluate the area in the search space that is not likely to find the optimal point. Low; although the Bayesian optimization method turns the black-box optimization scenario of machine lear...

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Problems solved by technology

However, there are various problems in these methods: the manual parameter tuning method has a strong dependence on experience; the automatic parameter tuning method is less efficient because it wastes time to evaluate the area in the search space that is unlikely to find the optimum point. Low; although the Bayesian optimization method turns the black-box optimization scenario of machine learning model hyperparameter tuning into a probabilistic proxy model, the solution result is easy to fall into a local optimal solution, and the solution result often depends on the selection of the initial value

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