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How is the kappa statistic used in classification?
Not only can this kappa statistic shed light into how the classifier itself performed, the kappa statistic for one model is directly comparable to the kappa statistic for any other model used for the same classification task. There is not a standardized interpretation of the kappa statistic.
What does Fleiss consider to be a good Kappa?
Fleiss considers kappas > 0.75 as excellent, 0.40-0.75 as fair to good, and < 0.40 as poor. It is important to note that both scales are somewhat arbitrary. At least two further considerations should be taken into account when interpreting the kappa statistic.
Is there a problem with κ as a classifier?
The issue with using κ in classification is p e and how to compute it. In your case, the most prevalent class is the true class with ( 45 + 25) / 100 records. A “classifier” that does nothing but assign all records to the majority class would already be right 70 % of the time.
Which is the correct confusion matrix for Kappa?
First, the kappa statistic should always be compared with an accompanied confusion matrix if possible to obtain the most accurate interpretation. Consider the following confusion matrix: The kappa statistic is 0.47, well above the threshold for moderate according to Landis and Koch and fair-good for Fleiss.
What does a 0.10 increase in Kappa mean?
If expected accuracy was 80%, that means that the classifier performed 40% (because kappa is 0.4) of 20% (because this is the distance between 80% and 100%) above 80% (because this is a kappa of 0, or random chance), or 88%. So, in that case, each increase in kappa of 0.10 indicates a 2% increase in classification accuracy.
When to use a down sample in machine learning?
In my opinion, the only reason to down-sample is when you have too much data and can’t fit your model. Many classifiers (logistic regression for example) will do fine on un-balanced data. As always @Marc Claesen as a great answer.
What is the difference between upweighting and downsampling?
Let’s start by defining those two new terms: Downsampling (in this context) means training on a disproportionately low subset of the majority class examples. Upweighting means adding an example weight to the downsampled class equal to the factor by which you downsampled.