Application of Machine Learning in Grey Generation Rate Index Analysis

Incorporating machine learning techniques, the analysis of correlation indices becomes an integral part of the new construction planning. Traditional grey relation models directly examine raw data, bypassing the need for in-depth processing of original data to uncover hidden patterns. This study employs a grey generation rate correlation analysis model that adheres to an exponential rule, accumulating grey raw data to generate a tectonic sequence generation rate, which characterizes the proximity and dynamic change trend of the original sequence similarity. Taking the new instance data as an example, the grey exponential law generation rate correlation model is applied. Through the historical data accumulation process, this model analyzes the connections between associated indicators and related factors, verifying the practicality and validity of the relational model based on the generation rate of grey quasi-index laws. This approach provides a reliable data instance for the government to formulate more reasonable strategies, enhancing the predictive power and precision with the integration of machine learning algorithms.