Topic 1 Question 254
3 つ選択A data scientist wants to improve the fit of a machine learning (ML) model that predicts house prices. The data scientist makes a first attempt to fit the model, but the fitted model has poor accuracy on both the training dataset and the test dataset.
Which steps must the data scientist take to improve model accuracy?
Increase the amount of regularization that the model uses.
Decrease the amount of regularization that the model uses.
Increase the number of training examples that that model uses.
Increase the number of test examples that the model uses.
Increase the number of model features that the model uses.
Decrease the number of model features that the model uses.
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コメント(10)
BCE B. Decrease the amount of regularization that the model uses: Regularization is used to prevent overfitting, but if the fitted model has poor accuracy on both the training and test datasets, reducing the amount of regularization can help the model better capture the underlying patterns and improve its accuracy.
C. Increase the number of training examples that the model uses: Increasing the number of training examples allows the model to learn from a larger and more diverse dataset, which can help improve its ability to generalize and make accurate predictions.
E. Increase the number of model features that the model uses: Adding more relevant features to the model can enhance its ability to capture important patterns and relationships in the data, leading to improved accuracy.
👍 7RRST2023/06/20- 正解だと思う選択肢: BCE
A. NO - regularization will reduce overfitting, not accuracy B. YES - to much regularization will reduce complexity and thus decrease accuracy C. YES - the more data the merrier D. NO - test examples will no influence model performance E. YES - the more features the more there is to learn F. NO - as per E
👍 3loict2023/09/12 - 正解だと思う選択肢: ACE
The problem is stating the Underfitting scenario. So correct answers are ACE
👍 2SandeepGun2023/06/17
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