Multinomial logistic regression

Imagine you are a kid trying to pick your favorite ice cream flavor. You have three choices: chocolate, vanilla, and strawberry. Now, imagine you have a friend who is also trying to pick their favorite flavor. BUT, their choices are different. They have five options: chocolate, vanilla, strawberry, mint chip, and rocky road.

Multinomial logistic regression is like figuring out which flavor you and your friend are likely to choose based on some information. Maybe it's based on things like the temperature outside or how hungry you are.

Now, the way we figure this out is by looking at a bunch of data (like a list of ice cream sales at an ice cream shop). We use this data to create a mathematical model that can help us predict which flavor you and your friend are most likely to choose based on certain factors.

But, because there are different choices for you and your friend, we have to use a different type of math called multinomial logistic regression. It's like a more advanced version of simple math that helps us predict multiple outcomes at once.

So, in summary, multinomial logistic regression is a type of math that helps us predict which option out of several choices is most likely based on some information. It's like choosing your favorite ice cream flavor, but for more complicated situations where there are different options for different people.