Additive smoothing

Additive smoothing is a way to estimate the probability of something happening, even if we don't have all the information. Imagine you have a bag with 10 marbles, 5 are red and 5 are blue. If you want to know the probability of getting a red marble without looking at the bag, you could guess it's 50%. But if you look at the bag and see that 3 of the marbles are red, the probability changes to 30%.

Additive smoothing helps us estimate probabilities when we don't have all the information. Let's say we have a bigger bag with 100 marbles, but we don't know how many are red or blue. We can start by assuming that each color has the same probability of being picked, which would be 50%. But we can also use additive smoothing to adjust this probability based on what we do know. For example, if we randomly pick 10 marbles and find 3 are red, we can use additive smoothing to estimate that the probability of picking a red marble from the whole bag is actually 33%, not 50%.

In essence, additive smoothing is a way to adjust a probability estimate based on the available data we have, making it more accurate even if it's not exact. Think of it like trying to guess the number of jellybeans in a jar, but adjusting your guess as you see others taking some out. As more information becomes available, we can use additive smoothing to refine our predictions and make them more accurate.