Do you think training with balanced batches is always better than using the original dataset proport
학생이 쓴 원문
Using balanced batch has an advantage that a network can train more accurately about minority class when training dataset is imbalance. For example, when we have imporant minority class such as health diagnosis and abnormal detection, using balanced bacth can help to improve performance.
However, if we only use the balanced batch, the network might be misunderstand dataset\'s proportion. At that time, the network think the number of minority class data is bigger than real environment. As a result, it can\'t reflect real dataset\'s population. Therefore, we should use a proper method depends on the target task.
강사 첨삭
You demonstrated strong conceptual understanding of balanced batches and class distribution. I especially like how you explained both the advantages and limitations, that shows analytical thinking. To improve, focus on grammar details (plural forms, verb tense, and word choice). Overall, your technical reasoning is clear and well-structured. Keep it up!
-Teacher Wela
Using balanced batch has an advantage that a network can train more accurately about minority class when training dataset is imbalance.
>> Using balanced batches has the advantage that a network can learn the minority class more accurately when the training dataset is imbalanced.
For example, when we have imporant minority class such as health diagnosis and abnormal detection, using balanced bacth can help to improve performance.
>> For example, when we have an important minority class, such as in health diagnosis or anomaly detection, using balanced batches can help improve performance.
However, if we only use the balanced batch, the network might be misunderstand dataset\'s proportion.
>> However, if we only use balanced batches, the network might misunderstand the dataset’s proportions.
At that time, the network think the number of minority class data is bigger than real environment.
>> In that case, the network may think that the number of minority class samples is larger than in the real-world environment.
As a result, it can\'t reflect real dataset\'s population.
>> As a result, it may not accurately reflect the real dataset’s distribution.
Therefore, we should use a proper method depends on the target task.
>> Therefore, we should choose an appropriate method depending on the target task.
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