Model improvement is a research area within the field of machine learning and data science that focuses on enhancing the performance and accuracy of predictive models. This involves fine-tuning model parameters, selecting the most appropriate algorithms, improving data preprocessing techniques, and incorporating new features or variables to improve the overall predictive power of the model. By continuously refining and optimizing models, researchers aim to achieve better accuracy, reliability, and generalizability in various applications such as forecasting, classification, regression, and clustering.