Great topic! When I test my machine learning models, I usually split the data into training and testing sets (sometimes even a validation set), and I rely heavily on techniques like cross-validation to avoid overfitting. I also use performance metrics such as accuracy, precision, recall, and AUC, depending on the model type.Interestingly, while working on a project related to predictive analytics in finance, I found a lot of parallels between evaluating models and assessing risks in economics. That’s when I started using platforms offering international finance assignment help to understand financial datasets better—it really sharpened my approach to feature selection and model interpretation