Application and Research Based on Random Forest Regression and CRITIC Methods

It is especially important to utilize existing data to enhance and monitor through a targeted and systematic approach, so this paper extracts some key data items based on valid data and analyzes their importance. Combining the random forest regression and CRITIC method to consider the importance, through the correlation heat map and the results of CRITIC for the initial screening of the characteristics, the use of weighting to get a comprehensive indicator to reflect the importance of the indicator. In addition, the sensitivity analysis yields that the model is more stable and can reasonably determine the key item data of the relevant research content, with a view to generating certain reference significance for the application and research in other fields.