Hyperparameter selection has always been the key to machine learning. The Bayesian optimization algorithm has recently achieved great success, but it has certain constraints and limitations in selecting hyperparameters. In response to these constraints and limitations, this paper proposed the N-RReliefF algorithm, which can evaluate the importance of hyperparameters and the importance weights between hyperparameters. The N-RReliefF algorithm estimates the contribution of a single hyperparameter to the performance according to the influence degree of each hyperparameter on the performance and calculates the weight of importance between the hyperparameters according to the improved normalization formula. The N-RReliefF algorithm analyses the hyperparameter configuration and performance set generated by Bayesian optimization, and obtains the important hyperparameters in random forest algorithm and SVM algorithm. The experimental results verify the effectiveness of the N-RReliefF algorithm.

中文翻译：

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基于N-RReliefF算法的超参数重要性分析

超参数选择一直是机器学习的关键。贝叶斯优化算法最近取得了巨大的成功，但是在选择超参数时有一定的约束和限制。针对这些约束和局限性，本文提出了一种N-RReliefF算法，该算法可以评估超参数的重要性以及超参数之间的重要性权重。N-RReliefF算法根据每个超参数对性能的影响程度来估计单个超参数对性能的贡献，并根据改进的归一化公式计算超参数之间的重要性权重。N-RReliefF算法分析贝叶斯优化生成的超参数配置和性能集，并获得了随机森林算法和支持向量机算法中的重要超参数。实验结果验证了N-RReliefF算法的有效性。