{"question": "A 6-sided die is rolled 15 times and the results are: side 1 comes up 0 times; side 2: 1 time; side 3: 2 times; side 4: 3 times; side 5: 4 times; side 6: 5 times. Based on these results, what is the probability of side 3 coming up when using Add-1 Smoothing?", "options": ["2.0/15", "1.0/7", "3.0/16", "1.0/5"], "answer": "B"}
{"question": "Which image data augmentation is most common for natural images?", "options": ["random crop and horizontal flip", "random crop and vertical flip", "posterization", "dithering"], "answer": "A"}
{"question": "You are reviewing papers for the World\u2019s Fanciest Machine Learning Conference, and you see submissions with the following claims. Which ones would you consider accepting? ", "options": ["My method achieves a training error lower than all previous methods!", "My method achieves a test error lower than all previous methods! (Footnote: When regularisation parameter \u03bb is chosen so as to minimise test error.)", "My method achieves a test error lower than all previous methods! (Footnote: When regularisation parameter \u03bb is chosen so as to minimise cross-validaton error.)", "My method achieves a cross-validation error lower than all previous methods! (Footnote: When regularisation parameter \u03bb is chosen so as to minimise cross-validaton error.)"], "answer": "C"}
{"question": "To achieve an 0/1 loss estimate that is less than 1 percent of the true 0/1 loss (with probability 95%), according to Hoeffding's inequality the IID test set must have how many examples?", "options": ["around 10 examples", "around 100 examples", "between 100 and 500 examples", "more than 1000 examples"], "answer": "D"}
{"question": "Traditionally, when we have a real-valued input attribute during decision-tree learning we consider a binary split according to whether the attribute is above or below some threshold. Pat suggests that instead we should just have a multiway split with one branch for each of the distinct values of the attribute. From the list below choose the single biggest problem with Pat\u2019s suggestion:", "options": ["It is too computationally expensive.", "It would probably result in a decision tree that scores badly on the training set and a testset.", "It would probably result in a decision tree that scores well on the training set but badly on a testset.", "It would probably result in a decision tree that scores well on a testset but badly on a training set."], "answer": "C"}