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 F1 score is closest to:
选项：
A.77%
81%
85%
解释：
B is correct. The model’s F1 score, which is the harmonic mean of precision and recall, is calculated as:
F1 score = (2 × P × R)/(P + R).
F1 score = (2 × 0.7778 × 0.8545)/(0.7778 + 0.8545) = 0.8143 (81%).
看不懂怎么做出来的。
