Consider the table given below. It contains the movie ratings of a single user. There are...

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Consider the table given below. It contains the movie ratings of a single user. There are 15 movies and we also have four movie features (romance, adventure, historical, horror). Each feature numerically represents how much the movie belongs to that genre. For example, M1 is a historical romance movie; there is almost no horror and adventure. However, the user has not rated some of the movies yet (shown with the question marks). In this task, you will be learning the preferences of a user and predict the rating that the user would give to unrated movies. Movie Rating Romance Historical Horror Adventure 4.9 0.8 0.06 0.96 0.001 M2 0.7 0.6 0.07 0.4 M3 0.3 0.88 0.001 0.0001 0.001 2.1 4.3 M4 0.001 0.67 0.9 0.001 0.02 M5 0.2 0.6 0.94 M6 0.5 0.2 0.94 M7 0.002 0.7 0.001 0.95 1 0.2 0.99 0.3 0.02 MS 0.001 0.98 M9 0.001 0.001 MI0 3.4 0.001 0.002 0.88 0.0001 M11 0.90 0.5 0.88 0.0001 M12 0.5 0.2 0.94 0.0001 0.001 0.5 0.3 M13 M14 M15 0.8 0.89 0.4 0.5 0.001 0.88 0.6 0.7 0.002 Note: This table is also provided to you in "Movie Rate.txt". Along with the answers to the following questions, you are expected to submit the code. You can use any class in scikit. a) [4pts] Is this a regression or classification problem? b) [8pts] Report learnt parameters. c) [8pts] What are your model's predictions for M2, M7 and M11? Consider the table given below. It contains the movie ratings of a single user. There are 15 movies and we also have four movie features (romance, adventure, historical, horror). Each feature numerically represents how much the movie belongs to that genre. For example, M1 is a historical romance movie; there is almost no horror and adventure. However, the user has not rated some of the movies yet (shown with the question marks). In this task, you will be learning the preferences of a user and predict the rating that the user would give to unrated movies. Movie Rating Romance Historical Horror Adventure 4.9 0.8 0.06 0.96 0.001 M2 0.7 0.6 0.07 0.4 M3 0.3 0.88 0.001 0.0001 0.001 2.1 4.3 M4 0.001 0.67 0.9 0.001 0.02 M5 0.2 0.6 0.94 M6 0.5 0.2 0.94 M7 0.002 0.7 0.001 0.95 1 0.2 0.99 0.3 0.02 MS 0.001 0.98 M9 0.001 0.001 MI0 3.4 0.001 0.002 0.88 0.0001 M11 0.90 0.5 0.88 0.0001 M12 0.5 0.2 0.94 0.0001 0.001 0.5 0.3 M13 M14 M15 0.8 0.89 0.4 0.5 0.001 0.88 0.6 0.7 0.002 Note: This table is also provided to you in "Movie Rate.txt". Along with the answers to the following questions, you are expected to submit the code. You can use any class in scikit. a) [4pts] Is this a regression or classification problem? b) [8pts] Report learnt parameters. c) [8pts] What are your model's predictions for M2, M7 and M11?

Answer rating: 100% (QA)

In the given table the individual rating is ... View the full answer