The Score
👹 I totally don’t like the statement that a model gives you the probability of an object being in some class. We want it. We are trying to achieve it. But you have to put TONS of effort into getting it.
📋 Also. For Titanic, we know that women were more likely to survive than men, first-class passengers more likely than third-class ones, and so on. Let’s just sum up good omens and subtract the bad ones. For five features, we will get a number in the range from -5 to 5. Is it useful? Yes. Is it a probability? No.
🎰🎰🎰 It is the Score. 🎰🎰🎰
For me, Score is a very important thing. In a binary classification task, it is some quantity that is large if we expect the object to belong to the positive class and small if we expect it to belong to the negative class.
Now let’s look at the whole picture. We have an object. We digitize it and send its features to a model. Then we move all these levers and knobs of the model so that it gives low score to objects from the negative class and high score to objects from the positive class.
👻 And here we are. We trained our model. Is that it? No.
We still have to set a threshold for decision-making and measure the quality of the model.