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# Defining a probability-based approach

Let's gradually introduce how logistic regression works. We said that it's a classifier, but its name recalls a regressor. The element we need to join the pieces is the probabilistic interpretation.

In a binary classification problem, the output can be either "0" or "1". What if we check the probability of the label belonging to class "1"? More specifically, a classification problem can be seen as: given the feature vector, find the class (either 0 or 1) that maximizes the conditional probability: Here's the connection: if we compute a probability, the classification problem looks like a regression problem. Moreover, ...

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