Understanding Confusion Matrix

The video tutorial explains how to use a confusion matrix to evaluate the performance of a classification model. It begins with an overview of various performance metrics, focusing on the confusion matrix. The process of dividing data into training and testing sets is discussed, followed by training the model. The structure and terms of the confusion matrix are explained, including true positives, true negatives, false positives, and false negatives. An example using a Decision Tree to predict football play is provided, and the results are analyzed using the confusion matrix.

Which of the following is NOT a performance metric for classification problems?

• Confusion Matrix

• Precision

• Recall

• Auto Sickleave

In a confusion matrix, what do the rows represent?

• Actual values or truth values

• Predicted values by the algorithm

• The accuracy of the model

• The number of samples

What is a true positive in the context of a confusion matrix?

• An instance where the model correctly predicts a negative outcome

• An instance where the model incorrectly predicts a positive outcome

• An instance where the model incorrectly predicts a negative outcome

• An instance where the model correctly predicts a positive outcome

In the football example, what does a false negative indicate?

• Zane didn't play football, but the model predicted he did

• Zane played football, but the model predicted he didn't

• Zane didn't play football, and the model predicted he didn't

• Zane played football, and the model predicted he did

Why is it important to have higher numbers in the green boxes of a confusion matrix?

• They represent the instances where the model made incorrect predictions

• They represent the instances where the model made correct predictions

• They indicate the number of samples tested

• They show the overall accuracy of the model