Topic 1 Question 191
You are developing a new deep learning model that predicts a customer's likelihood to buy on your ecommerce site. After running an evaluation of the model against both the original training data and new test data, you find that your model is overfitting the data. You want to improve the accuracy of the model when predicting new data. What should you do?
Increase the size of the training dataset, and increase the number of input features.
Increase the size of the training dataset, and decrease the number of input features.
Reduce the size of the training dataset, and increase the number of input features.
Reduce the size of the training dataset, and decrease the number of input features.
ユーザの投票
コメント(11)
- 正解だと思う選択肢: B
There 2 parts and they are relevant to each other
- Overfit is fixed by decreasing the number of input features (select only essential features)
- Accuracy is improved by increasing the amount of training data examples.
👍 6John_Pongthorn2022/09/26 - 正解だと思う選択肢: B
Option B Feature selection is the one the ways to resolve overfitting. Which means reducing the features when the size of the training data is small, then the network tends to have greater control over the training data. so increasing the size of data would help.
👍 2HarshKothari212022/09/07 - 👍 2TNT872022/09/07
シャッフルモード