MLS-C01 · Question #91
A Data Scientist is training a multilayer perception (MLP) on a dataset with multiple classes. The target class of interest is unique compared to the other classes within the dataset, but it does…
The correct answer is D. Add class weights to the MLP's loss function and then retrain. Adjust to higher weight for class of interest: https://androidkt.com/set-class-weight-for-imbalance- dataset-in-keras/. More data may/may not be available and a data labeling job will take time.
Question
A Data Scientist is training a multilayer perception (MLP) on a dataset with multiple classes. The target class of interest is unique compared to the other classes within the dataset, but it does not achieve and acceptable recall metric. The Data Scientist has already tried varying the number and size of the MLP's hidden layers, which has not significantly improved the results. A solution to improve recall must be implemented as quickly as possible. Which techniques should be used to meet these requirements?
Options
- AGather more data using Amazon Mechanical Turk and then retrain
- BTrain an anomaly detection model instead of an MLP
- CTrain an XGBoost model instead of an MLP
- DAdd class weights to the MLP's loss function and then retrain
How the community answered
(43 responses)- A5% (2)
- B16% (7)
- C21% (9)
- D58% (25)
Explanation
Adjust to higher weight for class of interest: https://androidkt.com/set-class-weight-for-imbalance- dataset-in-keras/. More data may/may not be available and a data labeling job will take time.
Topics
Community Discussion
No community discussion yet for this question.