Answer-first summary for fast verification
Answer: The model is overfitting in areas with less traffic and underfitting in areas with more traffic.
The most likely reason for the performance issue is that the model is overfitting in areas with less traffic and underfitting in areas with more traffic. This suggests that the training data did not include enough examples of highly congested traffic, leading the model to perform poorly in such scenarios. Overfitting occurs when a model learns the details and patterns in the training data to such an extent that it performs well on the training data but fails to generalize to new, unseen data. Underfitting occurs when the model has not learned enough from the training data, resulting in poor performance overall. Therefore, option A is the correct answer.
Author: LeetQuiz Editorial Team
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You recently built the first version of an image segmentation model for a self-driving car, which is meant to identify and separate different objects in traffic scenes from video input. After deploying the model, you observe a decrease in the area under the curve (AUC) metric, indicating a drop in performance. When analyzing the video recordings, you discover that the model performs well in scenarios with less traffic but fails in highly congested traffic conditions. What is the most likely reason for this performance issue?
A
The model is overfitting in areas with less traffic and underfitting in areas with more traffic.
B
AUC is not the correct metric to evaluate this classification model.
C
Too much data representing congested areas was used for model training.
D
Gradients become small and vanish while backpropagating from the output to input nodes.
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