The correct answer is False.
Variable importance is a measure of how important a variable is for predicting the target variable. It is typically calculated by measuring how much the model’s performance changes when the variable is removed.
For most classification models, each predictor will have a single variable importance, which is the same for all classes. This is because the model is trained to predict the target variable, not a specific class. Therefore, the importance of a variable is not dependent on the class that is being predicted.
However, there are some classification models that can have different variable importances for different classes. This is typically the case for models that are trained to predict a probability distribution over the classes. In these models, the importance of a variable can be different for different classes because the variable may be more important for predicting one class than another.
For example, consider a model that is trained to predict whether a customer will churn. In this model, the variable “number of years as a customer” may be more important for predicting customers who are likely to churn than customers who are not likely to churn. This is because customers who have been with the company for a long time are more likely to be loyal and less likely to churn.
In conclusion, the variable importance of a predictor is typically the same for all classes in most classification models. However, there are some classification models that can have different variable importances for different classes.