I am using Dice score to compare image segmentation.
But what is the significance of Kappa score, Positive predictive Value and Jaccard coefficient with respect to Image segmentation?
How can use these values to tune my algorithm?
The correctly and incorrectly classified instances show the percentage of test instances that were correctly and incorrectly classified. The raw numbers are shown in the confusion matrix, with a and b representing the class labels. Here there were 100 instances, so the percentages and raw numbers add up, aa + bb = 59 + 12 = 71, ab + ba = 27 + 2 = 29.
The percentage of correctly classified instances is often called accuracy or sample accuracy. It has some disadvantages as a performance estimate (not chance corrected, not sensitive to class distribution), so you'll probably want to look at some of the other numbers. ROC Area, or area under the ROC curve, is my preferred measure.
Kappa is a chance-corrected measure of agreement between the classifications and the true classes. It's calculated by taking the agreement expected by chance away from the observed agreement and dividing by the maximum possible agreement. A value greater than 0 means that your classifier is doing better than chance (it really should be!).
The error rates are used for numeric prediction rather than classification. In numeric prediction, predictions aren't just right or wrong, the error has a magnitude, and these measures reflect that.
Hopefully that will get you started.
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