# Suitable performance metric for an unbalanced multi-class classification problem?

I have an unbalanced multi-class classification problem with the following class distributions:

Class 0: 17.1%
Class 1: 63.2%
Class 2: 19.7%


I am using scikit-learn's Support Vector Classifier with 'balanced' class weights to classify samples into one of the three classes. However, I don't know what would be the most suitable performance metric to evaluate the result? So far I have been using F1-micro score, but not sure if this is the best option for multi-class imbalance problems?

There are many useful metrics which were introduced for evaluating the performance of classification methods for imbalanced data-sets. Some of them are Kappa, CEN, MCEN, MCC, DP, etc.

Disclaimer:

If you use python, PyCM module can help you to find and calculate these metrics.

Here is a simple code to get the recommended parameters from this module:

>>> from pycm import *

>>> cm = ConfusionMatrix(matrix={"Class1": {"Class1": 1, "Class2":2}, "Class2": {"Class1": 0, "Class2": 5}})

>>> print(cm.recommended_list)
["Kappa", "SOA1(Landis & Koch)", "SOA2(Fleiss)", "SOA3(Altman)", "SOA4(Cicchetti)", "CEN", "MCEN", "MCC", "J", "Overall J", "Overall MCC", "Overall CEN", "Overall MCEN", "AUC", "AUCI", "G", "DP", "DPI", "GI"]

>>> score = cm.Kappa