![]() ![]() Therefore, the F1-score is the best choice from among these four metrics. In incident detection, not only is it important to detect incidents, but also to avoid overwhelming the traffic operator with false incident detections. The F1-score is a trade-off between precision and recall. On the other hand, recall is more oriented to answering whether we can trust predicted positives. Tue 09.00 EDT Last modified on Tue 09.01 EDT A t one point in this unsettling paranoia thriller, a character warns protagonist James (Harry Shum Jr) that he’s at risk of. Precision might be good if you must detect as many positives as possible, even at the risk of getting lots of false positives. ![]() In such a situation, accuracy is not a good metric, not even balanced accuracy. A key point for choosing the final trade-off is the selection of the metric to evaluate the performance of the system, and this decision must be made by putting oneself in the skin of the user and asking if the chosen metric and the corresponding results are representative of the concept of usefulness.įor example, the following two tables are examples of confusion matrices of an imbalanced dataset with 9x more negative examples than positive examples, for example, no-incident vs incident. Broadcast Signal Intrusion movie review (2021) Roger Ebert Reviews Broadcast Signal Intrusion Brian Tallerico OctoTweet Now streaming on: Powered by JustWatch In November 1987, two stations in Chicago, Illinois were hijacked. SXSW horror/thriller Broadcast Signal Intrusion is based off actual broadcast interruptions that occurred in the late 80’s in Chicago, and still remain unsolved. Classification problems with highly unbalanced datasets, such as incident detection, pose the trade-off between true positive rate and false negative rate. Sometimes the eeriest horror movies of all are the ones inspired by real-life events.
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