Intro to ML: Evaluation (Part 2)
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Assessment
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Josiah Wang
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Computers
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University
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11 plays
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Medium
Student preview
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10 questions
Show answers
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
Answer explanation
Using k-fold cross validation is not a method to reduce overfitting, as it is a technique for assessing model performance. Stopping training earlier, using a larger dataset, and reducing the complexity of the model are all strategies to prevent overfitting by limiting the model's ability to memorize the training data.
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
Answer explanation
The size of the confidence interval for a model's error rate can be reduced by increasing the number of examples in your sample. More examples provide more data points, which in turn, increases the precision of the model's error rate estimation, thereby reducing the size of the confidence interval.
3.
Multiple Choice
Which of our data sets should we use to calculate the sample error of our model?
Train
Dev
Test
Answer explanation
The test data set should be used to calculate the sample error of the model. This is because the test set is specifically designed to evaluate the model's performance on unseen data, providing an unbiased estimate of its generalization ability. Using the train or dev sets would not give an accurate representation of the model's performance on new data.
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
Answer explanation
In the special case where the model sample error is zero, the size of the sample does not affect the confidence interval. This is because the error, which would normally be influenced by the sample size, is non-existent. Therefore, the confidence interval remains 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
Answer explanation
The question asks about methods to handle imbalanced datasets. Downsampling the majority class and using several metrics are both valid techniques. However, using k-fold cross validation is not a specific solution for dealing with an imbalanced dataset, making it the correct answer.
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