please answer all the questions thank you!
Consider the following set of 6 emails, which classify the email as spam or not. Which of the following rules might we learn? Check all that apply. Emails containing both flagged words "cash" and "free" are marked "spam". Emails without the flagged word "free" are marked "not spam". Emails with an even number of words are marked "spam". Emails with less than 31 words are marked "spam". Emails containing at least one flagged word in all capital letters are marked "spam". The company Intemet Movies, Inc. has found wide success in their streaming movie business. After many long and successful years of delivering content, they have decided to use machine learming to make their business even more successful. Luckily, they already possess a huge dataset that has grown over years and years of user activity - but they need your help to make sense of itl Answer the following questions. Let's start with a simple case. Assume user Alice is a particularly good member and she makes sure to rate every movie she ever watches on the website. What machine leaming approach would be better for the company to use for determining whether she would be interested in a new specifc movie? supervised unsupervised Bob, on the other hand, is not that much into ratings. He does watch a lot of movies, but never thices the time to rate them. For users like Bob, which of the following data can the company use to determine potential interest in a specific movie? Check all that apply. Metadata of movies: actors, director, genre, etc, Length of the movie Popularity of the movie amongst other users Jser login patterns What machine learning approach should the company use for cases like Bob? supervised unsupervised Now that the company has some idea about how to use the data, it's time to design a classifver. Our classifier will be very simple: given a movie and a user, it will classify the movie as either "Good" or "Bad" for this user. Assume all the users of the company have a very simple rule in their movie taste: they like it if Tom Cruise has the lead role. Any other data is mostly irrelevant. However, no one in the company knows about this fact. Which of the following clustering models might be able to detect this rule? Check all that apply. Supervised (label: rating), with data: Director, language, genre Supervised (label; rating), with data: Movie length, lead role, director Jnsupervised, with data: Lead role, movie length, rating Jnsupervised, with data: Lead role, genre, director Unsupervised, with data: Number of ratings, lead role Looking at the options they're given, the board members choose to go with a supervised model with lead role as data. You become outraged. "How can you not include movie length? It's incredibly important! Who watches a 3 hour long movie .." Your fellow data scientist interrupts you. "Yeah, I agree, but look at these initial results. You see, if we remove movie length, ..." What can your colleague (correctly) say to convince you? Check all that apply
