Assume that a classification model is trained to detect fraudulence emails from nonfraudulence ones, and we would like to test the classifier's efficacy in making such detections. A test data containing $40$ email instances are provided. The test data labels are given and denoted as gold$-$standard labels. The table below provides the gold standard and predicted labels for each data sample using this classifier. $"0"$ means non$-$fraudulence emails, and $"1"$ indicates fraudulence emails. Using this table, answer the following questions.

Note: Round your answers to two decimals: $.$

$\backslash$table$[[$instanse$,$gold$-$standard labels,model$-$predicted labels$],[1,1,1],[2,1,1],[3,0,0],[4,1,1],[5,0,1],[6,1,1],[7,1,0],[8,0,1],[9,1,1],[10,0,0],[11,1,1],[12,1,1],[13,0,0],[14,0,0],[15,1,1],[16,1,1],[17,0,0],[18,1,1],[19,1,1],[20,1,1],[21,1,1],[22,0,0],[23,1,1],[24,1,0],[25,0,1],[26,1,1],[27,0,0],[28,1,1],[29,1,0]]$