You work as a machine learning specialist for a software company developing a movie rating social media site where users can rate movies. You want to use your companies data to predict the rating distribution of a movie based on the genre of the movie. Your training data contains a genre feature with a set of categories such as documentary, romance, etc. You have sorted your data by the genre feature and then used the Amazon ML sequential split option to split your data into training and test datasets. 
When using your test dataset to verify your genre-prediction model, you discover that the accuracy rate is very low. What could be the underlying problem?

Answer options:

A.You should have sorted by a different feature before you used the sequential split option.
B.You should have split your data categorically by genre.
C.You should have split your data sequentially by year.
D.You should not have used the sequential split option.

Answer: D
Option A is incorrect. Sorting the data by a different feature wouldn’t solve the problem. You used the sequential option when splitting the data. So you have not properly randomized your data.
Option B is incorrect. By categorically splitting the data by definition, you will have some genre movies only in the training dataset and others only in the test dataset. This reduces the genre feature to a meaningless datapoint.
Option C is incorrect. Sequentially splitting the data by year wouldn’t solve the problem. You used the sequential option when splitting the data. So you have not properly randomized your data.
Option D is correct. You should not have used the sequential option when splitting your data. To get proper generalization from your data, you need to randomize it for this type of problem.
Reference:
Please see the Amazon Machine Learning developer guide titled Splitting Your Data.