Evaluation of Classification Models in Machine Learning

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.