This is the predictions of a binary classification model. The model is doing predicitons continuously, and these values are the sum of positive labels during a 10 hours period. As you can see, some of the locations are tend to generate positive labels, but really most of them are not true. The x-axis is location and the y-axis is time.
Is there a way, maybe, another model can learn the trends of locations (x-values) and with some kind of a reinforcement learning method, the model can learn that most of the positive predictions of these locations are actually false?
I don't know exactly how much of them are false positives, but I'm definitely sure really most of them are. Can I use this data to train another unsupervised model? Or even a smoothing algorithm maybe would work? Thank you.