[amp_mcq option1=”To visualize data” option2=”To build predictive models” option3=”To summarize data” option4=”To display the counts of true positive, true negative, false positive, and false negative predictions” correct=”option4″]
The correct answer is D. To display the counts of true positive, true negative, false positive, and false negative predictions.
A confusion matrix is a table that is used to evaluate the performance of a classification model. It is a square matrix with four cells, each of which represents the number of instances that were classified as a particular class. The four cells are:
- True positive (TP): The number of instances that were actually in the positive class and were correctly classified as such.
- True negative (TN): The number of instances that were actually in the negative class and were correctly classified as such.
- False positive (FP): The number of instances that were actually in the negative class but were incorrectly classified as positive.
- False negative (FN): The number of instances that were actually in the positive class but were incorrectly classified as negative.
The confusion matrix can be used to calculate a number of metrics that measure the performance of the classification model, such as accuracy, precision, recall, and F1 score.
Option A is incorrect because the confusion matrix is not used to visualize data. Option B is incorrect because the confusion matrix is not used to build predictive models. Option C is incorrect because the confusion matrix is not used to summarize data.