Part I:

What is the purpose of a confusion matrix in model evaluation?

a. to show the performance of a binary classifier.

b. to transform the data into a different format.

c. to normalize the values of the data to have a mean of zero and a standard devisation of one.

d. to visualize the distribution of the target variable in a dataset.

Part 2:

What is the purpose of a ROC curve in model evaluation?

a. to show the performance of a binary classifier.

b. to transform the data into a different format.

c. to normalize the values of the data to have a mean of zero and a standard devisation of one.

d. to visualize the distribution of the target variable in a dataset.

Part 3:

What is the purpose of a F1 score in model evaluation?

a. to evaluate the performance of a multi-class classifier.

b. to evaluate the performance of a binary classifier.

c. to evaluate the overall performance of a model.

d. to evaluate the performance of a regression model