Abstract The widespread use of machine learning algorithms in dataset modeling requires a thorough understanding of the various tools likely to improve the modeling quality. Any machine learning algorithm has two types of parameters: model parameters and hyperparameters. Parameters are adjusted in the learning process, while hyperparameters are defined a priori . Hyperparameter tuning is an essential element in the modeling process due to its effect on the quality of results. This study focuses on enhancing the modeling accuracy of mosquito species distribution in Morocco by optimizing the hyperparameters of the employed algorithms. Three tuning methods were selected for this purpose: Genetic Algorithms, Bayesian Optimization, and Particle Swarm Optimization. The experimental results confirmed the effect of hyperparameter tuning on the modeling quality, with accuracy improvements ranging from 0.02 to 0.067. In addition, Genetic Algorithms and Bayesian Optimization proved more effective than Particle Swarm Optimization. The hyperparameter tuning process has optimized the modeling quality, which can only enhance the explanation of mosquito distribution.
Douider et al. (Thu,) studied this question.