Topic 1 Question 109
A company is developing an ML model to predict customer churn. The model performs well on the training dataset but does not accurately predict churn for new data.
Which solution will resolve this issue?
Decrease the regularization parameter to increase model complexity.
Increase the regularization parameter to decrease model complexity.
Add more features to the input data.
Train the model for more epochs.
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コメント(4)
- 正解だと思う選択肢: B
Increase the regularization parameter to decrease model complexity.
Increasing the regularization parameter helps prevent overfitting by penalizing more complex models, encouraging the model to generalize better to new data.
Would you like more detailed information on how to implement this change or any other aspect of model tuning?
👍 226b8fe12024/12/26 - 正解だと思う選択肢: B
The most effective solution to resolve overfitting and improve the model’s performance on new data is B. Increase the regularization parameter. This helps make the model simpler, reducing the likelihood of overfitting and improving its ability to generalize.
👍 1aws_Tamilan2024/12/27 - 正解だと思う選択肢: B
The correct answer is B. Increasing the regularization parameter reduces model complexity and prevents overfitting.
👍 1may2021_r2024/12/28
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