Using SMOTE for Imbalanced Classification in Azure ML Designer
Question
For your classification task, you have a large but imbalanced dataset with class A and class B as labels.
Since in your database occurrences of B are relatively low, in order to have more accurate predictions, you decide to try doubling the percentage of the under-represented class.
You decide to try the SMOTE (Synthetic Minority Oversampling Technique) module available in the ML Designer, and you also want reproducible results.
Which settings should you use to get the expected result?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: B.
Option A is incorrect because setting the SMOTE percentage to 0 will generate no additional minority items, the dataset remains unchanged.
Option B is CORRECT because in order to double the percentage of the minority class in the dataset, you have to set the SMOTE percentage parameter to 200
It will result in twice as much percentage (not number of cases!) in the dataset as initially.
Setting the Random seed to the same, not null value for different runs guarantees reproducibility of the results.
Option C is incorrect because setting the SMOTE percentage to 200 actually gives the expected result, but leaving the Random seed will end in different results over several runs.
Option D is incorrect because SMOTE percentage = 100 will double the number of minority cases, which result in a higher percentage but the percentage is not necessarily doubled.
Reference:
In this scenario, the goal is to address the issue of class imbalance in the dataset and improve the accuracy of predictions. One approach to achieving this is to use the SMOTE (Synthetic Minority Oversampling Technique) module available in the Azure ML Designer. SMOTE is a widely-used oversampling technique that generates synthetic samples of the minority class to balance the dataset.
To ensure reproducible results, it is important to set a fixed random seed value. This ensures that the same set of random numbers is used each time the algorithm runs, which makes it easier to compare results and troubleshoot issues.
Based on the options given, the correct answer is D: Set SMOTE percentage = 100; set Random seed = 0.
Here's why:
A (Set SMOTE percentage = 0; set Random seed = 0): This option sets the SMOTE percentage to zero, which means that no oversampling will be performed. This will not address the issue of class imbalance. Additionally, setting the random seed to zero is fine for reproducibility, but it won't help address the class imbalance issue.
B (Set SMOTE percentage = 200; set Random seed = 1): This option sets the SMOTE percentage to 200, which means that the minority class will be oversampled by a factor of 2. This is a good step to address the issue of class imbalance. However, setting the random seed to 1 means that a different set of random numbers will be used each time the algorithm runs, which makes it harder to compare results and troubleshoot issues.
C (Set SMOTE percentage = 200; leave Random seed empty): This option sets the SMOTE percentage to 200, which means that the minority class will be oversampled by a factor of 2. This is a good step to address the issue of class imbalance. However, leaving the random seed empty means that a different set of random numbers will be used each time the algorithm runs, which makes it harder to compare results and troubleshoot issues.
D (Set SMOTE percentage = 100; set Random seed = 0): This option sets the SMOTE percentage to 100, which means that the minority class will be oversampled by a factor of 1. This is a good step to address the issue of class imbalance. Additionally, setting the random seed to zero ensures that the same set of random numbers is used each time the algorithm runs, which makes it easier to compare results and troubleshoot issues.
In conclusion, option D (Set SMOTE percentage = 100; set Random seed = 0) is the best choice for achieving both reproducibility and addressing class imbalance in the dataset.
