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Question 83 - Professional Machine Learning Engineer discussion

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You recently built the first version of an image segmentation model for a self-driving car. After deploying the model, you observe a decrease in the area under the curve (AUC) metric. When analyzing the video recordings, you also discover that the model fails in highly congested traffic but works as expected when there is less traffic. What is the most likely reason for this result?

A.
The model is overfitting in areas with less traffic and underfitting in areas with more traffic.
Answers
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.
Answers
B.
AUC is not the correct metric to evaluate this classification model.
C.
Too much data representing congested areas was used for model training.
Answers
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.
Answers
D.
Gradients become small and vanish while backpropagating from the output to input nodes.
Suggested answer: A

Explanation:

The most likely reason for the observed result is that the model is overfitting in areas with less traffic and underfitting in areas with more traffic. Overfitting means that the model learns the specific patterns and noise in the training data, but fails to generalize well to new and unseen data. Underfitting means that the model is not able to capture the complexity and variability of the data, and performs poorly on both training and test data. In this case, the model might have learned to segment the images well when there is less traffic, but it might not have enough data or features to handle the more challenging scenarios when there is more traffic. This could lead to a decrease in the AUC metric, which measures the ability of the model to distinguish between different classes. AUC is a suitable metric for this classification model, as it is not affected by class imbalance or threshold selection. The other options are not likely to be the reason for the result, as they are not related to the traffic density. Too much data representing congested areas would not cause the model to fail in those areas, but rather help the model learn better. Gradients vanishing or exploding is a problem that occurs during the training process, not after the deployment, and it affects the whole model, not specific scenarios.Reference:

Image Segmentation: U-Net For Self Driving Cars

Intelligent Semantic Segmentation for Self-Driving Vehicles Using Deep Learning

Sharing Pixelopolis, a self-driving car demo from Google I/O built with TensorFlow Lite

Google Cloud launches machine learning engineer certification

Google Professional Machine Learning Engineer Certification

Professional ML Engineer Exam Guide

Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

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asked 18/09/2024
Gopakumar Nair
39 questions
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