There are lots of metrics such r-square, MSE, MAE, MAPE and others. Some paper use r-square combining RMSE, some use EVS+MAPE+t-test+NSE, so if I want to evaluate a price prediction model or a conversion rate prediction model, what combination should I choose?
Dear Ling i have answered your question already .
we are faced with different metrics and indices but it's highly depend on your data type but you can use confusion matrix or all of your above mentioned metrics . also you can use statistical distance functions like Euclidean Distance, Manhattan Distance, Minkowski Distance and so on . try to define some them in your MATLAB or python code to ensure that which one is more accurate and sensitive in your case .
That being said, there are some common metrics that are frequently used in the evaluation of regression models, which can be useful for evaluating price and conversion rate prediction models as well. These metrics include:
The choice of metrics and their combination can depend on your specific goals and requirements. For example, if you are interested in the accuracy of the predictions, you may want to use metrics such as RMSE or MAE. If you are interested in the overall fit of the model, you may want to use metrics such as R² or EVS. If you are interested in the statistical significance of the predictions, you may want to use a t-test.
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