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- 1. Multiple Choice
Which of the following is not used to reduce overfitting?
Stopping the training earlier
Using a larger dataset
Reducing the complexity of the model
Using k-fold cross validation
- 2. Multiple Choice
Which of the following will certainly reduce the size of the confidence interval for a model's error rate?
Increasing the number of examples in your sample
Improving your model to reduce its error rate
- 3. Multiple Choice
Which of our data sets should we use to calculate the sample error of our model?
Train
Dev
Test
- 4. Multiple Choice
In the special case where our model sample error is zero, which of the following is true when using the sample error formula:
The confidence interval will depend on the sample size
The confidence interval will be the same regardless of the sample size
- 5. Multiple Choice
Which of the following is not a solution for dealing with an imbalanced dataset?
Downsample the majority class
Use several metrics, choosing the one that reflects the intended model behaviour
Use k-fold cross validation
- 6. Multiple Choice
Which of the following statements is false?
Overfitted models perform better on the training data than on the test data
Overfitting can occur when learning is performed for too long
Overfitting can occur if the training set is not representative
Underfitted models always generalise well to different datasets
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