Chapter 5

Why do we use evaluation metrics in machine learning?

• To train the model faster

• To measure the performance of a model

• To add more data

• To visualize the dataset

Which of the following is an evaluation metric for classification problems?

• MSE (Mean Squared Error)

• Accuracy

• MAE (Mean Absolute Error)

• RMSE (Root Mean Squared Error)

What does a confusion matrix show?

• Model weights

• Training speed

• Actual vs predicted values

• Feature importance

What is a true positive (TP) in a confusion matrix?

• A positive class correctly predicted as positive

• A negative class correctly predicted as negative

• A positive class predicted as negative

• A negative class predicted as positive

Which metric is calculated as the number of correct predictions divided by the total number of predictions?

• Precision

• Recall

• F1 Score

• Accuracy

What is precision?

• The proportion of predicted positives that are actually positive

• The proportion of actual positives that are predicted positive

• The total number of correct predictions

• The number of false positives

What is recall?

• The number of false negatives

• The proportion of actual positives that are predicted positive

• The number of true negatives

• The proportion of predicted positives that are actually positive

What is the F1 score?

• The sum of precision and recall

• The average of precision and recall

• The harmonic mean of precision and recall

• The difference between precision and recall

What is the main evaluation metric for regression problems?

• Accuracy

• F1 Score

• Confusion matrix

• Mean Squared Error (MSE)

What does RMSE stand for?

• Relative Mean Squared Error

• Root Mean Squared Error

• Regression Mean Sample Error

• Random Model Standard Error