I'm not big on statistics, so please excuse my ignorance.

I have a video recording that I want to evaluate, I have an algorithm that can transform this video into a time series where I have 0 everywhere except for a couple of frames where an event (A) occurs.

Then I have manual annotations of another event (B) that I think is related to the 1st event (A) (i.e. event B appears shortly after event A).

I want to construct a confusion matrix like this:

  1. Event A is condition, event B is "test"
  2. True positive value is when there is an event A and within 50 frames there is also event B
  3. False positive is when A is not present, but B is
  4. False negative is when A is present, but B is not

Now my problem is about True Negatives. If you do vaccine testing, you have a total number of tests, and you can quantify True Negatives without issue. But what about my case? I either have event A or B but True Negative is by definition everything else in the time series?

Does it make sense to even use confusion matrix?

  • $\begingroup$ Reading it more carefully, if you're using A as an input signal to predict event B, then your error types seem to be swapped - false positive would be when you predicted B (had seen A) but B didn't happen; and false negative would be when you didn't predict B (didn't see A), but B was actually present. So #3 and #4 would be opposite to what you have written now. $\endgroup$
    – Peteris
    May 20, 2014 at 7:11

1 Answer 1


Yes, the default value of 'no event found' would be a True Negative, just like any other "needle in a haystack" classification problem.

For binary classification usually you wouldn't call it a confusion matrix, the Precision and Recall terminology would be more clear.

  • $\begingroup$ Velcome to the site! $\endgroup$ May 19, 2014 at 9:55
  • $\begingroup$ Thanks, but the problem I have is that I can not quantify this 'no event found' $\endgroup$
    – mirosval
    May 20, 2014 at 5:40
  • $\begingroup$ @mirosval The idea is that you count these events for each point at the original time series, so your example class #4 would be at the points opposite to #2, i.e. when there is an event A, but within 50 frames there is not also event B. $\endgroup$
    – Peteris
    May 20, 2014 at 6:42
  • $\begingroup$ @Peteris Ah I should have mentioned that these are rare events (I think that's the term?) i.e I have less than 10 in about 6k frames. $\endgroup$
    – mirosval
    May 20, 2014 at 7:04
  • $\begingroup$ @mirosval in many problems such an imbalance is common (or stronger, like 10 in a million). That's the whole reason for treating precision and recall separately, to distinguish between types of error. If you have 10 real events to be detected, then in any case true positives+false negatives should be equal to 10; and you might be interested in what proportion of those 10 events you get correctly. $\endgroup$
    – Peteris
    May 20, 2014 at 7:09

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