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Diagnosing a medical imaging classifier with high training error.
Case context: You are training a machine learning model to classify medical images. After the initial training phase, you estimate the algorithm's bias to be 15% and its variance to be 1%. The model struggles to correctly classify the images it was trained on.
Question: Based on these metrics, how would you diagnose the model's current state, and how does it perform on the dev set relative to the training set?
Sample answer: The model's state should be diagnosed as underfitting, characterized by high bias and low variance. Because the variance is only 1%, the model's error on the dev set will be barely higher than its 15% training error.
Key points:
- The estimated bias of 15% and variance of 1% indicates high bias and low variance.
- The dev set error is barely higher than the training error.
- The algorithm is underfitting the data.
Rubric: The response must state that the model is underfitting (or has high bias/low variance) and note that the dev set error is only slightly higher than the training error.
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Machine Learning
Deep Learning
Machine Learning Strategy
Supervised Learning
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Machine Learning Yearning @ DeepLearning.AI
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Diagnosing a medical imaging classifier with high training error.
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