Low Accuracy Rate in Genre-Prediction Model
Question
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?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.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.
The underlying problem for the low accuracy rate on the test dataset could be due to several factors. Let's analyze each answer option:
A. Sorting by a different feature: Sorting by a different feature could lead to different data distributions in the training and test datasets. This can affect the performance of the genre-prediction model as it may not capture the relevant patterns in the data. However, this may not be the only cause of low accuracy.
B. Splitting the data categorically by genre: Categorical splitting would ensure that each genre has a balanced representation in both the training and test datasets. This would increase the likelihood of the model to learn relevant features for each genre, but it may not solve the issue entirely.
C. Sequential splitting by year: Splitting sequentially by year is only relevant if the data has a temporal structure, such as changes in movie genres over time. If this is not the case, this method of splitting may not be the most appropriate.
D. Not using sequential split option: Using a random split option may have resulted in an imbalanced representation of genres in the training and test datasets. This could make it more difficult for the model to learn relevant features for each genre. However, this may not be the only cause of low accuracy.
Overall, it's hard to determine the underlying problem for the low accuracy rate without analyzing the data in more detail. However, one possible solution could be to try categorical splitting to ensure each genre has a balanced representation in both datasets. It's also important to evaluate the model's performance using different metrics, such as precision and recall, to get a better understanding of its strengths and weaknesses.
