I want to know how to get several performance measurements of a generated WEKA model. Note that I am predicting a two-class variable,
Dead and I use the developer version
3.7.10 in my own Java project.
I know this is a simple question but am I correct that the one below is a direct equivalent of a traditional confusion matrix?
=== Confusion Matrix === a b <-- classified as 13735 216 | a = Alive 392 657 | b = Dead
TP | FN FP | TN
Sensitivity and specificity
Given a part of the WEKA result buffer below, below contains the ROC, specificity (or
Recall) and sensitivity (or
Precision) measurements but depending on a given class.
=== Detailed Accuracy By Class === TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class 0.985 0.374 0.972 0.985 0.978 0.665 0.974 0.998 Alive 0.626 0.015 0.753 0.626 0.684 0.665 0.974 0.775 Dead Weighted Avg. 0.959 0.349 0.957 0.959 0.958 0.665 0.974 0.982
Now, is it right to interpret that the overall ROC, specificity and sensitivity of the whole model are the ones in the