DataTerminology.comNEW
Also known as: TN
A correctly predicted negative instance in binary classification.
DESCRIPTION
True negatives are cases the model correctly identifies as belonging to the negative class. They contribute to metrics like specificity and accuracy. Understanding TNs helps balance thresholds to minimize false alarms. In imbalanced datasets, optimizing TN and related metrics is vital. TN counts populate the confusion matrix alongside TP, FP, and FN. Proper evaluation prevents misleading performance impressions.
ALTERNATIVE DEFINITIONS
Google Developers
A negative example that the model correctly predicted as negative.
Clear, task-agnostic wording of TN.
Wikipedia
True negatives (TN) are cases correctly identified as negative.
Places TN within the confusion matrix framework.
scikit-learn
In a confusion matrix, true negatives are samples correctly predicted as the negative class.
Defines TN in practical evaluation context.
RELATED TERMS
True Positive Type II Error Confusion Matrix Specificity Precision Recall Accuracy ROC Curve Threshold
COMPETING TERMS
KNOWLEDGE AREAS
Data Science AI Testing & Validation
TARGET AUDIENCE
Data Scientist AI Engineer Data Analyst BI Specialist
EXTERNAL SOURCES
- Wikipedia - Confusion matrix
- scikit-learn - Model evaluation metrics
- Google Developers - Classification outcomes tutorial
TOP QUESTIONS
Why do True Negatives matter in evaluation?
They determine specificity and overall accuracy, especially important in high-cost false positive domains like fraud detection.
Category: Relevancy
How do thresholds affect True Negatives?
Increasing the threshold can raise TNs but may also increase false negatives; choose thresholds based on business costs.
Category: Requirements
What’s a common mistake regarding True Negatives?
Focusing only on accuracy without considering class imbalance can misrepresent performance, masking low TN or TP rates.
Category: Mistakes
How are True Negatives used to compute specificity?
Specificity equals TN divided by TN plus FP, showing true negative rate.
Category: Testing
What is the risk of optimizing for more True Negatives?
You might increase false negatives, missing actual positive cases; balance depends on business costs.
Category: Risks
What is a competing term for True Negative?
False Positive, the incorrect prediction of a positive for a negative instance.
Category: Alternatives
Where to start learning about True Negatives?
Study confusion matrices, ROC curves, and cost-sensitive evaluation in classification.
Category: Example
How do True Negatives relate to ROC curves?
ROC curves visualize trade-offs between true positive rate and false positive rate, indirectly reflecting TN through FP counts.
Category: Opinion
Can calibration affect True Negatives?
Better-calibrated probabilities support optimal thresholding, potentially improving TN without harming TP.
Category: Requirements