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Questions and Answers
What does the term 'Recall' represent in the context of the given text?
What does the term 'Recall' represent in the context of the given text?
In the context of the model's accuracy, what does the term 'Precision' refer to?
In the context of the model's accuracy, what does the term 'Precision' refer to?
What is the accuracy of the model mentioned in the text?
What is the accuracy of the model mentioned in the text?
Based on the given information, what is a major drawback of the model?
Based on the given information, what is a major drawback of the model?
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Study Notes
Model Evaluation Metrics
- An evaluation metric quantifies the performance of a predictive model in supervised learning.
- In classification, predictions are either correct or wrong.
Accuracy
- Accuracy is the proportion of events correctly identified (positive or negative) on all events.
- Formula: Accuracy = (TP + TN) / (TP + TN + FP + FN)
- Example: Accuracy = (30 + 20) / (30 + 20 + 4 + 6) = 0.83
Error Rate
- Error rate is the proportion of events incorrectly identified (positive or negative) on all events.
- Formula: Error Rate = (FP + FN) / (TP + TN + FP + FN)
- Example: Error Rate = (4 + 6) / (30 + 20 + 4 + 6) = 0.167
- Lower error rate is better.
Precision
- Precision is the proportion of correctly positive events from all events identified as positive.
- Formula: Precision = TP / (TP + FP)
- Example: Precision = 30 / 30 = 1
- Higher precision is better.
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Description
Test your knowledge of model evaluation metrics in supervised learning with this quiz. Evaluate your understanding of performance quantification, classification predictions, and accuracy calculations.
