Vertaile menetelmiä

	Tarkkuusmatriisi	Tarkkuus	Tarkkuus
Tieteenala	Mallien arviointi	Mallien arviointi	Mallien arviointi
Menetelmäperhe	MCDM	MCDM	MCDM
Syntyvuosi	20th century	20th century	20th century
Kehittäjä	Statistical foundations	Historical statistical foundations	Historical statistical foundations
Tyyppi	Evaluation visualization	Evaluation metric	Evaluation metric
Alkuperäislähde	Everitt, B. S., & Hothorn, T. (2005). A Handbook of Statistical Analyses Using R. Chapman and Hall/CRC.	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.
Rinnakkaisnimet	Error Matrix, Contingency Table	Overall Accuracy, Correct Classification Rate	Positive Predictive Value, PPV
Liittyvät	5	5	5
Tiivistelmä	The confusion matrix is a table that displays the counts of true positives, true negatives, false positives, and false negatives. It provides a complete picture of where a classifier makes correct and incorrect predictions, enabling calculation of all other classification metrics.	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.	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.