![]() ![]() You don’t change your love/hate decisions, you just say you love/hate some movies a little more or less (formally, you give each of your friends a bootstrapped version of your original training data). So instead of giving your friends the same data you gave Willow, you give them slightly perturbed versions. Or maybe you told her you loved Cinderella, but actually you really really loved it, so some of your friends should give Cinderella more weight. After all, you’re not absolutely sure of your preferences yourself – you told Willow you loved Titanic, but maybe you were just happy that day because it was your birthday, so maybe some of your friends shouldn’t use the fact that you liked Titanic in making their recommendations. Now you don’t want each of your friends to do the same thing and give you the same answer, so you first give each of them slightly different data. That is, instead of asking only Willow, you want to ask Woody, Apple, and Cartman as well, and they vote on whether you’ll like a movie (i.e., you build an ensemble classifier, aka a forest in this case). ![]() In order to get more accurate recommendations, you’d like to ask a bunch of your friends and watch movie X if most of them say they think you’ll like it. Thus, Willow is a decision tree for your movie preferences.īut Willow is only human, so she doesn’t always generalize your preferences very well (i.e., she overfits). She asks more informative questions first (i.e., she maximizes the information gain of each question), and gives you a yes/no answer at the end. Then, when you ask her if she thinks you’ll like movie X or not, she plays a 20 questions-like game with IMDB, asking questions like “Is X a romantic movie?”, “Does Johnny Depp star in X?”, and so on. In order to answer, Willow first needs to figure out what movies you like, so you give her a bunch of movies and tell her whether you liked each one or not (i.e., you give her a labeled training set). Suppose you’re very indecisive, so whenever you want to watch a movie, you ask your friend Willow if she thinks you’ll like it. Here is a write up that I feel is the most simple way you can explain random forests.Ĭredits go to Edwin Chen for the simple explanation here in layman terms for random forests. Adding on to the above two answers, Since you mentioned a simple explanation. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |