Performance Metrics

$\begin{bmatrix} \textbf{""} & \textbf{Predicted True} & \textbf{Predicted False} \\\\ \textbf{Actual True} & 30 & 20 \\\\ \textbf{Actual False} & 5 & 40 \\\\ \end{bmatrix}$

1. Calculate the model’s precision, recall, and F1.
2. What can we do to improve the model’s performance?

1. If your model predicts A 100% of the time, what would the F1 score be? Hint: The F1 score when A is mapped to 0 and B to 1 is different from the F1 score when A is mapped to 1 and B to 0.
2. If we have a model that predicts A and B at a random (uniformly), what would the expected $F_1$ be?