Dear Shima Shafiee , you might want to cosider calcuating Cohen's Kappa. See for instance this paper:

https://www.knime.com/blog/cohens-kappa-an-overview

Shima Shafiee

I suggest Precision-Recall Metrics. The F-Measure is a well-known statistic for unbalanced classification.

Traditional model assessment approaches, on the other hand, do not effectively quantify model performance when confronted with unbalanced datasets. Standard classifier techniques, such as Decision Tree and Logistic Regression, favor classes with a large number of occurrences. They are only good at predicting data from the majority of classes.

DEAR Annika Hamachers

Thanks for sharing the link that is definitely effective and related to mentioned parameters.

GOOD LUCK

Qamar Ul Islam

I read your answers eagerly because they are very useful and complete

If you know the reference, please introduce it?

Thanks

GOOD LUCK

Hi Shima Shafiee

For unbalance data set most preferred evaluation matrix is ROC/AUC score.

For Unbalanced data use ROC/AUC, precision recall or use confusion matrix.

Hi

Masud Pervez,

Thanks for sharing your answer

and why?

GOOD LUCK

Hi

Ashish Mishra

Thanks for sharing your answer

and why? please explain if you have time

GOOD LUCK

Intersectoral distance, information distance, chi-squared

Hi

Eugene Veniaminovich Lutsenko

Thanks for sharing your answer and wonderful

http://ej.kubagro.ru/get.asp?id=8292&t=0