# quantify ML algorithm accuracy, reducing false negatives (i.e. breast cancer detection)

I'm doing some anomaly detection, basically classifying stuff as normal (0) or aberrant (1)

As with all anomalies, they are rare, so during the train/test phase it's not good enough for me to just look at general training error (which would be reduced to 10% if I just guessed class 0 (normal) all the time!)

with 90% non-aberrant data, I've decided to use A/B as my metric for training accuracy

where A = number of false negatives (saying it's normal when it's actually aberrant)

and B = # of positives (both false, and real)

So now, when I make a prediction, I also output that value (actually the average of that value when I rerun the test 15 times, and give each run an unweighted vote). And then I can say "oh, my test value is small enough, I can be confident in the prediction"

My question is whether or not this is sound practice, and if there are any better metrics for anomaly detection?

Here is the formula for $F_{\beta}$ score:
You want to compute $F_2$ score, which will penalize false negatives.