The accuracy is the ratio of correct results (true positives and true negatives) to total number of tests. If you classify the first six results in the table are "positive" and the rest are "negative", that gives 5 true positives and 9 true negatives. The accuracy is 14/20, which is higher than any other threshold point on the curve.
If you use this classification rule, 5/6 of the data you classify as "positive" are correct, but only 9/14 of those you classify as "negative" are really negative. Seeing an observation classified as "positive" is more trustworthy than seeing a "negative" classification.
score $\geq .54$ is the threshold that classifies the top 6 scores as "positive".
A balanced distribution of the score would imply that true negatives get high scores just as often as true positives get low scores. And in this example it looks like we're assuming that the proportions are .50-.50 positive and negative.
vafisher
- 487
- 3
- 5