Problem #$8$:

$(1$ point$)$ Evaluating the performance of a classification model involves using various

metrics to understand different aspects of its predictions. Consider a binary classification

problem where the predicted labels are compared against the true labels to form a

confusion matrix.

Given the following confusion matrix for a binary classifier:

Predicted Positive Predicted Negative

Actual Positive TP FN

Actual Negative FP TN

$3$

Answer the following questions:

$$ Given the values TP $=50,$ FN $=10,$ FP $=5,$ and TN $=100,$ calculate the

precision, recall, F$1-$score, and accuracy.

$$ Discuss a scenario where high precision is more critical than high recall and

explain whyProblem #$8$:

$(1$ point$)$ Evaluating the performance of a classification model involves using various

metrics to understand different aspects of its predictions. Consider a binary classification

problem where the predicted labels are compared against the true labels to form a

confusion matrix.

Given the following confusion matrix for a binary classifier:

Answer the following questions:

Given the values $TP=50,FN=10,FP=5,$ and $TN=100,$ calculate the

precision, recall, F$1-$score, and accuracy.

Discuss a scenario where high precision is more critical than high recall and

explain why.