Hi @maryam_nourmand,

If you use a model to predict continuous numerical values, some models enable you to directly save the residuals in your data table by clicking on the red triangle, choose "Save Columns", and then "Residuals". For Neural Networks, you'll need some manual work, saving the prediction formula in your table (red triangle, "Save Formulas" or "Save Fast Formulas"), and then creating a new column "Residuals" with a formula : Actual value - Predicted value

Note that you already have options to plot "Residual by Predicted" graph in the Neural Network platform, when clicking on the red triangle.

If you use a model to predict classes, you won't have residuals but you have information about misclassification and calculated probabilities. You can then save these probabilities columns (same options than before), and try to understand where are the errors, for example by plotting the probabilities of the main class predicted and see if the classification probability threshold needs to be adjusted.

If you are interested about evaluating classification models, there are also various plots that helps you evaluate the performance of your classifier model : ROC Curve, Lift curve, confusion matrix, ...

Hope this answer will help you,

Victor GUILLER

Scientific Expertise Engineer

L'Oréal - Data & Analytics