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Also known as: TP
A correctly predicted positive instance in binary classification.
DESCRIPTION
True positives are cases the model correctly identifies as belonging to the positive class. They drive metrics like recall, sensitivity, and F1 score. Balancing TPs against false positives requires careful thresholding. In high-sensitivity domains, maximizing TPs helps detect vital cases. TN, TP, FP, and FN counts form the confusion matrix. Understanding TPs is essential for cost-sensitive decision-making.
ALTERNATIVE DEFINITIONS
Google Developers
A positive example that the model correctly predicted as positive.
Direct, task-agnostic definition of TP.
Wikipedia
True positives (TP) are cases correctly identified as positive.
Defines TP within the confusion matrix context.
scikit-learn
In binary classification, true positives are samples correctly predicted as the positive class.
Introduces TP alongside precision and recall metrics.
RELATED TERMS
True Negative Type I Error Confusion Matrix Recall Precision F1 Score 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 - Classification metrics overview
- Google Developers - Tutorial on classification outcomes
TOP QUESTIONS
Why are True Positives important?
They indicate correct detection of positive cases, underpinning recall and sensitivity, crucial in safety-critical applications.
Category: Relevancy
How do thresholds change True Positive counts?
Lower thresholds often increase TPs but may raise false positives; select thresholds with business cost considerations.
Category: Requirements
What’s a common mistake with True Positives?
Maximizing TPs without regard for precision can flood systems with false alarms and degrade trust.
Category: Mistakes
How are True Positives measured?
They are counted against ground truth labels in a confusion matrix and used in recall and F1 computations.
Category: Testing
What’s the risk of optimizing only True Positives?
Precision may suffer, increasing costs from false alarms; balance depends on domain needs.
Category: Risks
What is a competing term for True Positive?
False Negative, the missed detection of a positive case.
Category: Alternatives
Where to start learning about True Positives?
Study confusion matrices, precision-recall trade-offs, and threshold tuning techniques.
Category: Example
Can calibration improve True Positives?
Yes; calibrated probabilities support better thresholding for desired TP-FP balance.
Category: Requirements
How do True Positives affect F1 score?
Higher TPs typically raise recall and can improve F1 if precision remains adequate.
Category: Opinion