TP, FP, TN, FN, Accuracy, Precision, Recall, F1-Score, Sensitivity, Specificity, ROC, AUC 

You're most welcome

Thank you very much!

Thank you! Cheers!

Thanks!

Thank you for the positive feedback

Thank you for the positive feedback. I'll do my best.

Thank you for the positive feedback :) The most simple way to think of a threshold is through a simple model, with only 1 feature as an input and two possible classes as output (say 0 and 1). Then when the model is trained it finds the "best" threshold for the input feature. So, for example, if we denote the input feature as a, then the model may learn that if a>=0.5 then the label is 0, otherwise it's 1. In this example the threshold is 0.5. Neural networks work differently (in most cases) and thus thinking of a threshold may be confusing. For our example from the paragraph above, the output of a neural network will be a vector/list of size 2, where each index is the probability that the output is in that specific class. For example if the output is [0.14, 0.85] then the model "thinks" that there is a 14% chance that the input is from label 0 and a 85% chance its from label 1. If our neural network had only 1 neuron then the 0.5 value from the example above could be incorporated into it. "How do we change it?" - This really depends on what you want to achieve. If FA are more important than FN, or the other way around then you can change your loss function and the incorporated threshold will change accordingly. Hope this helps :)

@@ml_explained2155 cool, thanks 👍 Tbh, I didn't really get how can we "vary the thresholds" to further plot ROC or AUC for a neural network. . I mean, when a model is trained - we have only a single point at the ROC plot (current state of the model). But then how can we "change the thresholds" to have multiple points on the plot? Thank you 😊

You're welcome :) When we train the model, it outputs probabilities. We can change the threshold of those probabilities to get different labels - for example, if the model's output is [0.34, 0.66], one threshold could be if the threshold for class 1 is set to 50% then the output of the model is labeled 1. If we set the threshold to, say 70% then the output changes to 0. So playing around with the threshold gives you different outcomes for the ROC curve.

@@ml_explained2155 oh, okay, so ROC and AUC are mostly used for the binary classification?

Yes. exactly

Thank you :)