Mathews Correlation Coefficient
06/01/11 21:29
Recently I have been looking into various different classification metrics for a binary classification problem. One of the most useful single value metrics I have come across is the Mathews Correlation Coefficient (MCC). Its main advantage over that of accuracy, or percentage of correct predictions, is the ability to handle unbalanced classes.
When looking into this I decided it might be nice to see a 3D representation of the MCC and see how its shape is effected by changing the ratio of the classes. So here is a flash animation of an MCC. Plotted along the two axis are TP and TN. The Number of Positives (NP) and Number of Negatives (NN) are varied from 1 to 100. Here 1-MCC is plotted so that 0 is perfect prediction and 2 is perfect anti-prediction. 1 is equivalent to random guessing.
When looking into this I decided it might be nice to see a 3D representation of the MCC and see how its shape is effected by changing the ratio of the classes. So here is a flash animation of an MCC. Plotted along the two axis are TP and TN. The Number of Positives (NP) and Number of Negatives (NN) are varied from 1 to 100. Here 1-MCC is plotted so that 0 is perfect prediction and 2 is perfect anti-prediction. 1 is equivalent to random guessing.
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