In a classification problem, what does a confusion matrix represent?
Practice Questions
1 question
Q1
In a classification problem, what does a confusion matrix represent?
The relationship between features
The performance of a classification model
The distribution of data points
The training time of the model
A confusion matrix represents the performance of a classification model by showing the true positive, true negative, false positive, and false negative counts.
Questions & Step-by-step Solutions
1 item
Q
Q: In a classification problem, what does a confusion matrix represent?
Solution: A confusion matrix represents the performance of a classification model by showing the true positive, true negative, false positive, and false negative counts.
Steps: 8
Step 1: Understand that a classification problem involves predicting categories or classes for data points.
Step 2: Know that a confusion matrix is a table used to evaluate the performance of a classification model.
Step 3: Learn the four key terms in a confusion matrix: True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN).
Step 4: True Positive (TP) means the model correctly predicted the positive class.
Step 5: True Negative (TN) means the model correctly predicted the negative class.
Step 6: False Positive (FP) means the model incorrectly predicted the positive class when it was actually negative.
Step 7: False Negative (FN) means the model incorrectly predicted the negative class when it was actually positive.
Step 8: The confusion matrix displays these counts in a structured format, allowing you to see how well the model is performing.
