4 Questions
What does the term 'Recall' represent in the context of the given text?
The proportion of events identified as positive on all positive events
In the context of the model's accuracy, what does the term 'Precision' refer to?
The ability of the model to correctly identify positive cases
What is the accuracy of the model mentioned in the text?
0.9991
Based on the given information, what is a major drawback of the model?
Low recall
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.
Test your knowledge of model evaluation metrics in supervised learning with this quiz. Evaluate your understanding of performance quantification, classification predictions, and accuracy calculations.
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