Exam Professional Machine Learning Engineer topic 1 question 122 discussion

AOC makes sense in a binary classification problem, but that's not the case here. That is the biggest red flag right there in the question.

Overfitting and Underfitting: Overfitting describes a model that performs well on the training data but struggles with unseen data. Underfitting signifies a model that hasn't learned enough patterns from the training data. Congested Traffic as Unseen Data: If the training data primarily consisted of scenarios with less traffic, the model might not have been exposed to enough examples of congested situations. This would make congested traffic act like unseen data, leading to underfitting and poor performance.

very tricky, here is my view: A. The model is overfitting in areas with less traffic and underfitting in areas with more traffic. Most Voted > I dont think so because: "the model fails in highly congested traffic but works as expected when there is less traffic" which means it is NOT OVERFITTING with less traffic. Actually the contrary would make more sense. B. AUC is not the correct metric to evaluate this classification model. > My option. Image Segmentation is about ditinguishing objects, not sure AUC is right for this. C. Too much data representing congested areas was used for model training. > Cant be. THat would actually make it perform at least as good. D. Gradients become small and vanish while backpropagating from the output to input nodes. > No clue.

D. Gradients become small and vanish while backpropagating from the output to input nodes. This issue is known as the vanishing gradient problem, which can occur during the training of deep neural networks. In highly congested traffic scenes, there might be complex patterns and details that the image segmentation model needs to capture. However, if the model architecture is too deep and the gradients become very small during backpropagation, the model may struggle to update its weights effectively to learn these complex patterns. As a result, the model may fail to correctly segment objects in congested traffic scenes, leading to a decrease in performance. Vanishing gradients can prevent the model from effectively learning representations and features in the deeper layers of the network. It's possible that the model is working fine in less congested areas because the patterns are simpler and easier to learn, allowing the gradients to propagate more effectively.

Went with A

The most likely reason for this result is the model is overfitting in areas with less traffic and underfitting in areas with more traffic. Probably because the model was trained on a dataset that did not have enough examples of congested traffic. As a result, the model is not able to generalise well. When the model is validated on congested traffic, it makes mistakes because it has not seen this type of data before.

Answer A

the model was trained with bias

A It's an example of overfitting/underfitting problem

I vote A, it is likely that the model was trained on data that included mostly images of less congested traffic, and therefore did not generalize well to images of more congested traffic.