But it is not limited to simply showing you what errors your classification model is making. To put this simply, the confusion matrix shows you the manners in which your classification model is getting confused when it makes predictions. If you calculate a confusion matrix, you would get a better idea of what your classification model is getting right and what errors it is making. It is also used to measure AUC-ROC curves. The table contains four different combinations of predicted and actual values.Ī confusion matrix is very useful to measure Precision, Recall, Specificity, and Accuracy. The confusion matrix is essentially a table that describes the performance of a classification model by using a test dataset for which the actual values are known. If you only look at classification accuracy, you might not get the right picture, as there is the possibility of it being misleading in situations where you have an unequal number of observations in each class or you have upwards of two classes in your dataset. You could say that a confusion matrix is essentially a technique that can be used for summarizing the performance of a classification algorithm. It can be put to use against binary classification problems as well as multiclass classification problems. Of 0, if all positive cases are classified incorrectly.A confusion matrix is a measure used to solve classification problems. AnyĬlassifier evaluated using equations 7, 8 or 9 will have a measure value In equation 9, b has a value from 0 to infinityĪnd is used to control the weight assigned to TP and P. Mean ( g-mean) (Kubat et al., 1998), as defined in equationsħ and 8, and F-Measure (Lewis and Gale, 1994), as defined in equation Other performance measures accountįor this by including TP in a product: for example, The classifier missed all positive cases. If the systemĬlassifies them all as negative, the accuracy would be 99.5%, even though Of which are negative cases and 5 of which are positive cases. Than the number of positive cases (Kubat et al., 1998). Performance measure when the number of negative cases is much greater The accuracy determined using equation 1 may not be an adequate Positive cases that were correct, as calculated using the equation: The false negative rate ( FN) is the proportion of positivesĬases that were incorrectly classified as negative, as calculated usingįinally, precision ( P) is the proportion of the predicted Of negatives cases that were classified correctly, as calculated using The true negative rate ( TN) is defined as the proportion The false positive rate ( FP) is the proportion of negativesĬases that were incorrectly classified as positive, as calculated using Of positive cases that were correctly identified, as calculated using the The recall or true positive rate ( TP) is the proportion The accuracy ( AC) is the proportion of the total number of Several standard terms have been defined for the 2 class matrix: d is the number of correct predictions that an instance is positive.c is the number of incorrect of predictions that an instance negative, and.b is the number of incorrect predictions that an instance is positive,.a is the number of correct predictions that an instance is negative,.The entries in the confusion matrix have the following meaning in the context of our study: The following table shows the confusion matrix for a two class classifier. Performance of such systems is commonly evaluated using the data in the matrix. A confusion matrix (Kohavi and Provost, 1998) contains information aboutĪctual and predicted classifications done by a classification system.
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