Imagine you're working on a binary classification problem, and the model is performing well in terms of accuracy but poorly in terms of recall. What might be the issue and how would you address it?
- Issue with data imbalance; Use resampling techniques
- Issue with precision; Improve accuracy
- Threshold is too high; Lower the threshold
- Threshold is too low; Increase the threshold
The issue might be that the threshold for classification is set too high, causing true positives to be misclassified as false negatives, reducing recall. Lowering the threshold may help in improving recall without sacrificing too much precision.
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