Topic 1 Question 233
An online advertising company is developing a linear model to predict the bid price of advertisements in real time with low-latency predictions. A data scientist has trained the linear model by using many features, but the model is overfitting the training dataset. The data scientist needs to prevent overfitting and must reduce the number of features.
Which solution will meet these requirements?
Retrain the model with L1 regularization applied.
Retrain the model with L2 regularization applied.
Retrain the model with dropout regularization applied.
Retrain the model by using more data.
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コメント(4)
- 正解だと思う選択肢: A👍 3sevosevo2023/03/18
- 正解だと思う選択肢: A
Yes L1 for feature reduction
👍 2blanco7502023/03/20 - 正解だと思う選択肢: A
L1 shrinks the less important feature’s coefficient to zero thus, removing some feature altogether. So, this works well for feature selection in case we have a huge number of features.
👍 2GiorgioGss2023/03/21
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