Machine Learning Algorithms from Start to Finish in Python: Logistic Regression

Going back to our example, let's assume that the Lakers were having a terrible season(clearly not the case), and out of 20 games, they only won 1. so the odds to the Lakers winning would be: We can make a simple observation: the worse they play, the more close their odds of winning will be to 0. Concretely, when the odds are against them winning, then the odds will range between 0 and 1. Now let's look at the opposite. In other words, when the odds are for the Lakers winning, they begin at 1 and they can go all the way up to infinity. Clearly, there is a problem here. This asymmetry makes it hard to compare the odds for or against Lakers winning.