What is a confusion matrix?
A confusion matrix is a table used to evaluate the performance of a classification model by comparing predicted and actual values.
How do I read the values in a confusion matrix?
The confusion matrix contains four key values: True Positives, True Negatives, False Positives, and False Negatives.
What do True Positives and False Negatives mean?
True Positives are correctly predicted positive cases, while False Negatives are actual positives that were incorrectly predicted as negative.
Can you give me an example of a confusion matrix?
Sure! For a binary classification, a confusion matrix might look like this:
| | Predicted Positive | Predicted Negative | |----------------|--------------------|--------------------| | Actual Positive | 50 | 10 | | Actual Negative | 5 | 35 |
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