Random forests improve catalytic research by providing a more accurate and reliable method for data analysis. They are particularly useful for handling datasets with many variables and for identifying complex interactions between variables. This capability is crucial in catalysis, where the performance of a catalyst can depend on multiple factors that interact in non-linear ways.
