Confusion Matrix

By Kai Ming Ting. Encyclopedia of Machine Learning, 2024

808 citations recorded by the source corpus.

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Deep Transfer Learning for Image‐Based Structural Damage Recognition (2018, background): “In predictive analytics, classiﬁcation accuracy for true prediction alone may not be very reliable if the data set is unbalanced or there are more than two classes in the data set, but confusion matrix (Kohavi and Provost, 1998) can give a better way to evaluate results.”
Adversarial Attack and Defense on Graph Data: A Survey (2018, background): “Readers can refer to [131] for detailed explanations of those metrics.”
Image‐based post‐disaster inspection of reinforced concrete bridge systems using deep learning with Bayesian optimization (2018, background): “Figure 10 gives the confusion matrices (Kohavi & Provost, 1998) for both training and testing sets of images to demonstrate the accuracies and misclassiﬁcation errors for each scenario.”
Selecting critical features for data classification based on machine learning methods (2020, neutral): “Apart from looking at the confusion matrix, the assessment of the goodness of a classifier’s prediction can be seen from the Receiver Operating Characteristic (ROC) [102, 103] and Area Under the Curve (AUC) curves [104].”