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Linear and regression tree models are often used as graphical representations of data. Linear models are used in the decision making process as a valuable source of information. As for regression tree models, their value lies in the graphical representation of complex models. But both linear and tree models are often complex and not easy to understand. Improving the comprehensibility of both has advantages for the user as he/she can comprehend the model more easily. This thesis main subject is determining when a linear model or a tree model has reached it's point when it is no longer comprehensible.

Comprehensibility. --- Linear models. --- Regression tree models.