Topic 1 Question 81
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?
Gather more data using Amazon Mechanical Turk and then retrain
Train an anomaly detection model instead of an MLP
Train an XGBoost model instead of an MLP
Add class weights to the MLP's loss function and then retrain
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コメント(12)
For me answer is D, 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.
👍 34joep212021/09/27I believe is C, because we already made all changes possible in MLP hidden layers and the results have not improved then we must change model so XGBoot seems the best option
👍 5rhuanca2022/05/22In case of the quickest possible way, D seems fine. For XGBoost, it will take a bit of time to code again
👍 4ksarda112021/10/24
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