Evaluation of Classification Models in Machine Learning

Abstract

We study the problem of evaluation of different classification models that are used in machine learning. The
reason of the model evaluation is to find the optimal solution from various classification models generated in an
iterated and complex model building process. Depending on the method of observing, there are different measures
for evaluation the performance of the model. To evaluate classification models the most direct criterion that can be
measured quantitatively is the classification accuracy. The main disadvantages of accuracy as a measure for evaluation
are as follows: neglects the differences between the types of errors and it dependent on the distribution of class in the
dataset. In this paper we discussed selection of the most appropriate measures depends on the characteristics of the
problem and the various ways it can be implemented.