Bandingkan metode

	Presisi	Akurasi	Skor-F1
Bidang	Evaluasi Model	Evaluasi Model	Evaluasi Model
Keluarga	MCDM	MCDM	MCDM
Tahun asal	20th century	20th century	1979
Pencetus	Historical statistical foundations	Historical statistical foundations	C. J. van Rijsbergen
Tipe	Evaluation metric	Evaluation metric	Evaluation metric
Sumber perintis	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.	Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861-874.	van Rijsbergen, C. J. (1979). Information Retrieval (2nd ed.). Butterworth-Heinemann.
Alias	Positive Predictive Value, PPV	Overall Accuracy, Correct Classification Rate	F-measure, Harmonic Mean
Terkait	5	5	5
Ringkasan	Precision measures the proportion of positive predictions that were actually correct. It answers the question: 'Of all the cases we predicted as positive, how many were truly positive?' Precision is critical in scenarios where false positives are costly.	Accuracy is the proportion of correct predictions among the total number of predictions made by a classification model. It is the most intuitive performance metric and measures how often the classifier makes correct predictions overall, regardless of class.	The F1-score is the harmonic mean of precision and recall, providing a single metric that balances both concerns. It was introduced by van Rijsbergen in information retrieval and has become a standard metric for evaluating classification models where both precision and recall are important.