Learn Before
How to choose between precision and recall?
According to the formula, since we always want to maximize True Positive, the key point is whether we want to minimize False Positive (choose precision) or minimize False Negative (choose recall).
Cases where we want to minimize FP include crime detection, recommendation and so on, where we don't want to falsely detect a positive situation.
Cases where we want to minimize FN include prisoning, tumor detection and so on. The cost of falsely predicting a negative situation is very large.
However, the final decision depends on the actual needs that are specified.
0
3
Tags
Data Science
Related
Confusion Matrix
ROC Curve and ROC AUC
Precision and Recall performance metrics.
F1 Score
Optimizing Criteria in Classification Problems
Satisficing Criteria in Classification Problems
Bayes error rate
What evaluation metric would you want to maximize based on the following scenario?
Recall of a Classification Model
Precision of a Classification Model
Sensitivity Analysis of a Classification Model
Learning Curve of a Classification Model
Having three evaluation metrics makes it harder for you to quickly choose between two different algorithms, and will slow down the speed with which your team can iterate. True/False?
If you had the four following models, which one would you choose based on the following accuracy, runtime, and memory size criteria?
Coverage
How to choose between precision and recall?
F-Measure