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Class 1 accuracy refers to the proportion of correctly classified instances of a specific class (Class 1) in a classification task, relative to the total instances of that class. It is calculated by dividing the number of true positives (correctly predicted Class 1 instances) by the sum of true positives and false negatives (instances that belong to Class 1 but were misclassified). This metric is particularly useful in evaluating model performance in imbalanced datasets, where one class may dominate the others. High class 1 accuracy indicates that the model is effectively identifying instances of Class 1.

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