不用考虑class 0吗？

问题如下：

Once satisfied with the final set of features, Steele selects and runs a model on the training set that classifies the text as having positive sentiment (Class “1” or negative sentiment (Class “0”). She then evaluates its performance using error analysis. The resulting confusion matrix is presented in Exhibit 2.

Exhibit 2 Confusion Matrix

Based on Exhibit 2, the model’s precision metric is closest to: 

选项：

A.

78%

B.

81%

C.

85%

解释：

A is correct. Precision, the ratio of correctly predicted positive classes (true positives) to all predicted positive classes, is calculated as:

Precision (P) = TP/(TP + FP) = 182/(182 + 52) = 0.7778 (78%).