so entropyjust like it is in physics and thermodynamicsis measure of dataset' disorderof how same or different the dataset is so imagine we have dataset of different classificationsfor exampleanimals let' say have bunch of animals that have classified by species nowif all of the animals in my dataset are an iguanai have very low entropy because they're all the same but if every animal in my dataset is different animali have iguanas and pigs and sloths and who knows what elsethen would have higher entropy because there' more disorder in my dataset things are more different than they are the same entropy is just way of quantifying that sameness or difference throughout my data soan entropy of implies all the classes in the data are the samewhereas if everything is differenti would have high entropyand something in between would be number in between entropy just describes how same or different the things in dataset are now mathematicallyit' little bit more involved than thatso when actually compute number for entropyit' computed using the following expressionso for every different class that have in my datai' going to have one of these termsp and so on and so forth through pnfor different classes that might have the just represents the proportion of the data that is that class and if you actually plot what this looks like for each termqj mo qjit'll look little bit something like the following graph