Evaluating Machine Learning Models

Which of the following best describes a confusion matrix in machine learning?

• A table that shows the accuracy of a model

• A table that summarises the performance of a classification algorithm by showing true positives, false positives, true negatives, and false negatives

• A graph that plots precision against recall

• A chart that displays the loss function over time

In a binary classification problem, what does a 'false positive' mean?

• The model correctly predicts the negative class

• The model incorrectly predicts the positive class when it is actually negative

• The model correctly predicts the positive class

• The model incorrectly predicts the negative class when it is actually positive

Which formula correctly calculates precision?

Recall is also known as:

• Specificity

• Sensitivity

• Accuracy

• Precision

Which of the following best describes the F1 score?

• The arithmetic mean of precision and recall

• The geometric mean of precision and recall

• The harmonic mean of precision and recall

• The sum of precision and recall

What is the formula for the F1 score?

What does the ROC curve plot?

• Precision vs Recall

• True Positive Rate vs False Positive Rate

• Accuracy vs Loss

• True Negative Rate vs False Negative Rate

What does the area under the ROC curve (AUC) represent?

• The probability that the model will rank a randomly chosen positive instance higher than a randomly chosen negative one

• The sum of true positives and true negatives

• The difference between precision and recall

• The number of false positives

Which of the following is a sign of overfitting in a machine learning model?

• High accuracy on training data but low accuracy on test data

• Low accuracy on both training and test data

• High accuracy on both training and test data

• Low accuracy on training data but high accuracy on test data

Underfitting occurs when:

• The model is too complex and learns noise in the data

• The model performs well on training data but poorly on new data

• The model is too simple to capture the underlying pattern in the data

• The model has high variance