Machine learning models have been widely used in many applications in almost all areas of social life. Random forest is a supervised machine learning model that combines the results of multiple decision trees to achieve a single result using closure. Due to the ease of use and flexibility of the random forest machine learning model, there has been a push for its adoption in practical applications of both regression and classification problems. To fit the random forest machine learning model to different problems, the model parameters must be adjusted. Choosing the best parameter configuration for the model has a direct impact on the model’s performance. In this article, the parameters of the random forest model and parameter optimization algorithms are studied in detail. Furthermore, the study also tested different benchmark datasets to compare the performance of random forest model parameter optimization methods.