I have a multi-class machine learning problem for which I will try different methods on such as logistic regression, decision trees, multilayer perceptron etc. The observations in the data set have an attribute which is an index from 1-5 which defines how important it is that a certain observation gets correctly classified (index 1 very important, 5 not important at all). Question 1: How should I emphasize to the models that the lower index observations have greater importance? I am thinking of duplicating these observations so the models fit the lower index observations more well, what other approaches are possible? Question 2: What performance evaluation criterias can I use to find the models that predict these low index observations well?