This type of problem is confusingly named because regression, as we have seen, implies a continuously valued label, such as the median price of a house, or the height of a tree.
This is not the case with logistic regression. When you have a problem requiring logistic regression, it means that the label is categorical; for example, zero or one, True or False, yes or no, cat or dog, or it may more than two categorical values; for example, red, blue or, green, or one, two, three, four, or five, or the type of a given flower. The labels normally have probabilities associated with them; for example, P(cat=0.92), P(dog=0.08). Thus, logistic regression is also known as classification.
In our next example, we will use logistic regression to predict the category of items of fashion using the fashion_mnist dataset.
Here are a few examples:
