Suppose I had two coins and I flipped both of them. The possible combinations can be two heads, two tails, or one of each. These combinations are all part of a sample space.
Now let’s take this a step further. Taking the above demonstration, we want to determine the probability that each combination would occur.
Assuming independence, we derive the following probabilities:
All of these probabilities belong in a probability distribution.
In my previous post, I introduced a class of algorithms for solving classification problems. I also mentioned that Naive Bayes is based off of Bayes’ theorem. In this post, I will derive Naive Bayes using Bayes’ theorem.
In my previous post, you saw the derivative of the cost function for logistic regression as:
I bet several of you were thinking, “How on Earth could you derive a cost function like this:
Into a nice function like this:
Well, this post is going to go through the math. Even if you already know it, it’s a good algebra and calculus problem. Continue Reading