Topic 1 Question 271
A machine learning (ML) specialist is training a multilayer perceptron (MLP) on a dataset with multiple classes. The target class of interest is unique compared to the other classes in the dataset, but it does not achieve an acceptable recall metric. The ML specialist varies the number and size of the MLP's hidden layers, but the results do not improve significantly.
Which solution will improve recall in the LEAST amount of time?
Add class weights to the MLP's loss function, and then retrain.
Gather more data by using Amazon Mechanical Turk, and then retrain.
Train a k-means algorithm instead of an MLP.
Train an anomaly detection model instead of an MLP.
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コメント(5)
- 正解だと思う選択肢: A
Option A allows the ML specialist to add class weights to the MLP’s loss function, and then retrain. Class weights are a way of assigning different importance or penalties to different classes in a classification problem. Class weights can help balance the data distribution and reduce the bias towards the majority classes. Class weights can also help improve the recall metric, which is the ratio of true positives to the sum of true positives and false negatives. Recall measures how well the model can identify the relevant instances of a class, especially when the class is rare or unique. The ML specialist can use class weights to increase the importance or penalty of the target class of interest, and then retrain the MLP to improve its recall.
👍 4Mickey3212023/08/04 - 正解だと思う選択肢: A
A as this is Faster solution.
👍 2ADVIT2023/07/07 - 正解だと思う選択肢: A
Apologies for the confusion but on second thoughts, A is the right answer as unique doesn't mean unknown and this is still a supervised learning problem. Adding weights to classes would even out the bias caused by unique class and improve recall as mentioned by other experts in this forum. Please ignore my previous comment. A is the correct option indeed.
👍 2backbencher20222023/10/01
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