Intro to ML: Evaluation (Part 1)
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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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15 plays
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Medium
Student preview
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10 questions
Show answers
1.
Multiple Choice
Our model predicts which people have Covid-19. The model correctly predicts 100 people who have Covid-19, but it misses 50 people who have the illness and falsely predicts that 10 people have the illness when they don't. What is the precision of the model?
91%
66%
Not enough information
Answer explanation
The precision of a model is calculated as the number of true positives (100 in this case) divided by the sum of true positives and false positives (100+10). This gives us 100/110 = 0.909, which is approximately 91%. Therefore, the correct answer is 91%.
2.
Multiple Choice
A diagnostic test is used to identify individuals with a certain disease. The test correctly identifies 120 individuals with the disease, but fails to identify 30 individuals who actually have it, and incorrectly identifies 15 individuals as having the disease. What is the recall of the test?
80%
75%
Not enough information
Answer explanation
Recall is calculated as true positives divided by the sum of true positives and false negatives. Here, recall = 120 / (120 + 30) = 120 / 150 = 0.8 or 80%. Thus, the correct answer is 80%.
3.
Multiple Choice
If we predict True for every observation, what will our model recall be?
Not enough information
0%
100%
Answer explanation
The recall of a model is calculated as the number of true positives divided by the sum of true positives and false negatives. If we predict True for every observation, there will be no false negatives, hence the recall will be 100%. This is because we are correctly identifying all positive cases, even though we may also be incorrectly identifying some negative cases as positive.
4.
Multiple Choice
Which of the following do we use a test set for?
We use it to evaluate new features that we could add to our model
We use it for hyper-parameter tuning
We use it for held-out performance evaluation
None of the above
Answer explanation
The test set is used for held-out performance evaluation. This means it is used to assess the performance of a model on unseen data, providing an unbiased evaluation of the final model fit. It is not used for hyper-parameter tuning or evaluating new features for the model.
5.
Multiple Choice
When is cross validation particularly helpful?
When we have a small dataset
When we have a large dataset
When we are particularly constrained on computational resources
Answer explanation
Cross validation is particularly helpful when we have a small dataset because it allows us to maximize the use of limited data for both training and validation. By dividing the dataset into multiple folds and iteratively training and validating on different subsets, we can obtain a more accurate estimate of the model's performance and reduce the risk of overfitting.
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