Exam AWS Certified Machine Learning - Specialty topic 1 question 158 discussion

Isn't it A? the model doesn't classify C & D well.

the correct answer should be A, the model is clearly unable to tell C and D apart the reason why B is incorrect is subtle - there is holdout validation or cross-validation, but not holdout cross-validation; while I think it would be more reasonable to use CV with such a small dataset rather than holdout, the answer is mixing terms and therefore should be wrong also, the test set confusion matrix is still pretty comparable to the train set one, so I wouldn't say there is objective evidence to claim holdout is a wrong choice here

I would go for A as well.

I think option A is correct as C & D are behaving similarly.

I think the answer is D. A => C, D are similar in train but the testing results contradict that. There are many As and Bs for C

These results indicate that the model is overfitting for classes B and E, meaning that it is memorizing the specific features of these classes in the training data, but failing to capture the general features that are applicable to the test data. Overfitting is a common problem in machine learning, where the model performs well on the training data, but poorly on the test data3. Some possible causes of overfitting are: The model is too complex or has too many parameters for the given data. This makes the model flexible enough to fit the noise and outliers in the training data, but reduces its ability to generalize to new data

Actually, both A and D are true. It would be an easy one if we had to choose two answers. But we need to choose only one. So how to make sure that the person who created this question thought about A only? Also if we take a look into the test confusion matrix. We can see that the A class also missed with C class at the same rate as the C and D classes. I would even say that here the model is generally overfitted. I would go for B

Letter A is correct. The model gets confused between (C) and (D) in training and testing.

Selected Answer: B Hold-out Hold-out is when you split up your dataset into a ‘train’ and ‘test’ set. The training set is what the model is trained on, and the test set is used to see how well that model performs on unseen data. A common split when using the hold-out method is using 80% of data for training and the remaining 20% of the data for testing. Hold-out Hold-out is when you split up your dataset into a ‘train’ and ‘test’ set. The training set is what the model is trained on, and the test set is used to see how well that model performs on unseen data. A common split when using the hold-out method is using 80% of data for training and the remaining 20% of the data for testing. Refere: https://medium.com/@eijaz/holdout-vs-cross-validation-in-machine-learning-7637112d3f8f

Model in unable to tell c&D

D - Training accuracies of B and E are higher than those of test, whereas A has similar accuracy in both. For C and D, test accuracy has actually improved.

I think the answer is A. The model doesn't perform well on class C and D in both training and testing dataset. I don't think B is relevant to the question(cross-validation is not mentioned in the question)

What means holdout cross validation. There should be holdout validation vs cross validation

B should be the correct answer