You work for a maintenance company and have built and trained a deep learning model that identifies defects based on thermal images of underground electric cables. Your dataset contains 10,000 images,

Question

Options

• ACalculate the Area Under the Curve (AUC) value.
• BCalculate the number of true positive results predicted by the model.
• CCalculate the fraction of images predicted by the model to have a visible defect.
• DCalculate the Cosine Similarity to compare the model's performance on the test dataset to the

• A
  77% (24)
• B
  3% (1)
• C
  6% (2)
• D
  13% (4)

Explanation

A is correct because it is scale-invariant. AUC measures how well predictions are ranked, rather than their absolute values. AUC is also classification-threshold invariant. It measures the quality of the model's predictions irrespective of what classification threshold is chosen. B is incorrect because calculating the number of true positives without considering false positives can lead to misleading results. For instance, the model could classify nearly every image as a defect. This would result in many true positives, but the model would in fact be a very poor C is incorrect because merely calculating the fraction of images that contain defects doesn't indicate whether your model is accurate or not. D is incorrect because this metric is more commonly used in distance-based models (e.g., K Nearest Neighbors). This isn't an appropriate metric for checking the performance of an image classification model. https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc https://developers.google.com/machine-learning/clustering/algorithm/advantages-disadvantages

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