Glossary term
Glossary term
Foundations
In a binary classification, a number between 0 and 1 that converts the raw output of a logistic regression model into a prediction of either the positive class or the negative class. Note that the classification threshold is a value that a human chooses, not a value chosen by model training.
A logistic regression model outputs a raw value between 0 and 1. Then:
If this raw value is greater than the classification threshold, then the positive class is predicted.
If this raw value is less than the classification threshold, then the negative class is predicted.
For example, suppose the classification threshold is 0.8. If the raw value is 0.9, then the model predicts the positive class. If the raw value is 0.7, then the model predicts the negative class.
The choice of classification threshold strongly influences the number of false positives and false negatives.
Click the icon for additional notes.
See Thresholds and the confusion matrix in Machine Learning Crash Course for more information.
For example, suppose the classification threshold is 0.8. If the raw value is 0.9, then the model predicts the positive class. If the raw value is 0.7, then the model predicts the negative class.
The choice of classification threshold strongly influences the number of false positives and false negatives.
Click the icon for additional notes.
Created for this library
A fraud team raises the classification threshold during a high-risk period to reduce false declines, accepting some additional fraud losses.
A medical screening team lowers the classification threshold for an early-stage cancer model to favor recall over precision in triage.
A subscription business calibrates the classification threshold of its churn model so the daily call list size matches retention team capacity.
Definition source: Google for Developers Machine Learning Glossary | Creative Commons Attribution 4.0 License