Confusion Matrix and Naive Bayesian Classifier

Which of the following represents the number of correctly predicted positive observations in a confusion matrix?

• True Negatives (TN)

• False Positives (FP)

• True Positives (TP)

• False Negatives (FN)

In a confusion matrix, what does False Positive (FP) indicate?

• The model correctly predicted a positive outcome

• The model incorrectly predicted a positive outcome

• The model correctly predicted a negative outcome

• The model incorrectly predicted a negative outcome

Which metric can be derived from a confusion matrix to measure the accuracy of a model?

• Precision

• Recall

• F1 Score

• All of the above

In a confusion matrix, which value represents the number of actual positive observations that were incorrectly classified as negative?

• True Positives (TP)

• False Positives (FP)

• True Negatives (TN)

• False Negatives (FN)

How is precision calculated using a confusion matrix?

• TP / (TP + FN)

• TN / (TN + FP)

• TP / (TP + FP)

• FN / (FN + TN)

Which of the following best describes the True Negative (TN) value in a confusion matrix?

• The number of correctly predicted negative observations

• The number of incorrectly predicted negative observations

• The number of correctly predicted positive observations

• The number of incorrectly predicted positive observations

What is the primary purpose of a confusion matrix in classification problems?

• To visualize the performance of a classification model

• To calculate the loss function

• To adjust the hyperparameters of a model

• To select the best algorithm

Which metric from the confusion matrix is used to evaluate the model's sensitivity or true positive rate?

• Precision

• Recall

• Accuracy

• Specificity

If a model has high True Positives (TP) and True Negatives (TN), what can be said about the model's accuracy?

• The model has high accuracy

• The model has low precision

• The model has high recall

• The model has high specificity

In the context of a confusion matrix, which metric is most appropriate for imbalanced datasets?

• Accuracy

• Precision

• F1 Score

• Specificity