Data Science and Machine Learning Essentials – Pop Quiz 1

by Fuyang

The company Internet 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 learning 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 it! Answer the following questions

1. 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 learning approach would be better for the company to use for determining whether she would be interested in a new specific movie?

  • Supervised
  • Unsupervised


2. Bob, on the other hand, is not that much into ratings. He does watch a lot of movies, but never takes 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 among other users
  • User login patterns


3. 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 classifier. 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.

4. 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
  • Unsupervised, with data: Lead role, genre, director
  • Unsupervised, with data: Number of ratings, lead role


5. 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.

  • “we can reduce inter-cluster dissimilarity.”
  • “we can reduce intra-cluster dissimilarity.”
  • “the model starts to work. It doesn’t work otherwise.”
  • “we can consume less memory, and the results look almost the same.”


1. Supervised

2. Choose:

  • Metadata of movies: actors, director, genre, etc.
  • Popularity of the movie among other users

Explanation:Length of the movie may or may not be considered. We are more likely to consider it if we consider very long movies or very short movies as well.

3. Unsupervised

4. Choose:

  • Supervised (label: rating), with data: Movie length, lead role, director
  • Unsupervised, with data: Lead role, movie length, rating

Explanation: For supervised learning, we already have ratings for each label. So for the data to differentiate between those with Tom Cruise or not, we must have the leading role as one of our features. For unsupervised learning, we must have the lead role as part of our data. In addition, we must have a way to know whether users like the movie or not, so we must also know the rating.

5. Choose:

  • “we can reduce intra-cluster dissimilarity.”
  • “we can consume less memory, and the results look almost the same.”

Explanation: Movie lengths are not sufficiently different between most movies in order to help us in making the clusters. Reducing intra-cluster dissimilarity is correct because having less features allows us to differentiate elements within a cluster from each other. Consuming less memory with similar results is correct because we do not need as much storage and will save money. The supervised learning model will work and has no limits on the number of features allowed.

Note: above content is from edX.