Logistic regression
As previously discussed, our classification problem is best modeled with the probabilities that are bound by 0 and 1. We can do this for all of our observations with a number of different functions, but here we will focus on the logistic function. The logistic function used in logistic regression is as follows:
If you have ever placed a friendly wager on horse races or the World Cup, you may understand the concept better as odds. The logistic function can be turned to odds with the formulation of Probability (Y) / 1 - Probability (Y). For instance, if the probability of Brazil winning the World Cup is 20 percent, then the odds are 0.2 / 1 - 0.2, which is equal to 0.25, translating to odds of one in four.
To translate the odds back to probability, take the odds and divide by one plus the odds. The World Cup example is thus 0.25 / 1 + 0.25, which is equal to 20 percent. Additionally, let's consider the odds ratio. Assume that the odds of Germany winning the Cup are 0.18....
