Ridge regression-based modeling for assessing the extent of response to biological invasions

This study aims to construct a ridge regression-based model for assessing the threat level of biological invasion security. By comprehensively collecting data from economic, medical, scientific research, social and ecological domains and combining correlations, key factors closely related to the threat of biological invasion security are filtered out. Compared with linear regression, ridge regression can effectively solve the problem of covariance in the data, prevent the model from overfitting, and improve the stability and generalization ability of the model through the regularization term. The innovation is that this paper is the first time to apply the ridge regression technique to the assessment of biological invasion security threat, which fills the research gap in this field. Pearson correlation was used in the study, and several factors such as nominal GDP, total population, average annual temperature, energy conservation and environmental protection expenditures, and scientific research output were screened as independent variables, and the quantitative relationship with the number of biological invasive species species and invasion was constructed by using the ridge regression model. Through the validation of the model and data testing, the results show that the model is able to accurately assess the situation of different regions in response to biological invasions, providing a scientific basis for the development of targeted prevention strategies. In addition, another innovation of this study is the comprehensive consideration of economic, ecological, scientific research and other factors from the degree to ensure that the model can accurately reflect the multiple influencing factors of biological invasions, which provides an important reference for policy making and risk management in related fields.