In a binary classification problem, what does a confusion matrix represent?
Practice Questions
1 question
Q1
In a binary classification problem, what does a confusion matrix represent?
The relationship between features
The performance of the model on training data
The true positive, false positive, true negative, and false negative counts
The distribution of the target variable
A confusion matrix summarizes the performance of a classification model by showing the counts of true positives, false positives, true negatives, and false negatives.
Questions & Step-by-step Solutions
1 item
Q
Q: In a binary classification problem, what does a confusion matrix represent?
Solution: A confusion matrix summarizes the performance of a classification model by showing the counts of true positives, false positives, true negatives, and false negatives.
Steps: 8
Step 1: Understand that a confusion matrix is a table used to evaluate the performance of a classification model.
Step 2: Know that in binary classification, there are two classes: positive and negative.
Step 3: Identify the four key terms in a confusion matrix: true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN).
Step 4: True Positives (TP) are the cases where the model correctly predicts the positive class.
Step 5: False Positives (FP) are the cases where the model incorrectly predicts the positive class when it is actually negative.
Step 6: True Negatives (TN) are the cases where the model correctly predicts the negative class.
Step 7: False Negatives (FN) are the cases where the model incorrectly predicts the negative class when it is actually positive.
Step 8: The confusion matrix displays these counts in a 2x2 table format, helping to visualize the model's performance.
