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If model $M_1$ has higher accuracy than model $M_2$, the kappa of $M_1$ will always be higher than the kappa of $M_2$?

I want to proof or refute (an example for the same case where increasing accuracy provokes a decrease in kappa is enough) it.

As requested I will reference concepts:

Accuracy: https://en.wikipedia.org/wiki/Evaluation_of_binary_classifiers

Kappa: https://en.wikipedia.org/wiki/Cohen%27s_kappa

Models: https://en.wikipedia.org/wiki/Machine_learning

Both accuracy and kappa are pretty common concepts in machine learning. I just want to demonstrate or refute the above question.

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    $\begingroup$ This question lacks context. What is “accuracy”, “kappa” and what are the “models”? Even if an expert in machine learning comes across this, it is still better to just include definitions to be sure... $\endgroup$ Dec 13 '19 at 22:29
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A new answer since, as @vbn pointed out, I mislabeled my computations. (I could have edited my former answer if I hadn't deleted it too quickly).

The answer is No, you can even have smaller kappa despite having bigger accuracy :

Consider a population of 100 animals, 60 dogs and 40 cats. Say $M_1$ makes 25 correct dog predictions and 35 correct cat predictions. Write $p_o$ for the relative observed agreement among raters, and $p_e$ for the hypothetical probability of chance agreement. One gets $$ p_o(M_1) = \frac{25 + 35}{100} = 0.6 $$ $$ p_e(M_1) = \frac{(25+35)(25+5) + (35+35)(5+35)}{100^2} = 0.46 $$ Hence, $\kappa(M_1) = \frac{p_o(M_1) - p_e(M_1)}{1 - p_e(M_1)} \simeq 0.26$.

Now, $M_2$ makes 45 correct dog predictions and 16 correct cat predictions. The accuracy of M2 is slightly better, but the kappa drops : $$p_0(M_2) = \frac{45 + 16}{100} = 0.61$$ $$p_e(M_2) = \frac{(45+15)(45+24) + (15+16)(24+16)}{100^2} = 0.538$$ Hence, $\kappa(M_2) \simeq 0.16$.

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