Learn the meaning of No Risk Predicted and No Risk Observed in a confusion matrix for IBM AI Fundamentals. Explore the significance of true positives and confusion matrix terms in AI evaluation.
Table of Contents
Question
Look at the confusion matrix generated by your AI model. Which of the following best describes the cell representing No Risk Predicted and No Risk Observed?
A. True negative
B. False negative
C. False positive
D. True positive
Answer
D. True positive
Explanation
The confusion matrix shows that your AI model scores well on true positives. This means that your AI model is very good at accurately predicting “No Risk” for people who are not a risk.
In the context of a confusion matrix, a “True Positive” (TP) occurs when the model’s prediction aligns with the observed reality for a specific class—in this case, “No Risk.”
From the matrix:
- The cell labeled as “No Risk Predicted and No Risk Observed” (295) indicates cases where the model correctly identified “No Risk” when it was indeed observed as “No Risk.”
- This is a correct prediction for the “No Risk” class, qualifying it as a True Positive (TP).
Key Definitions in the Confusion Matrix Context:
- True Positive (TP): Correctly predicted as the positive class (e.g., No Risk in this context).
- True Negative (TN): Correctly predicted as the negative class.
- False Positive (FP): Incorrectly predicted as the positive class when it is actually negative.
- False Negative (FN): Incorrectly predicted as the negative class when it is actually positive.
Understanding these definitions helps to interpret model performance metrics, such as precision, recall, and accuracy.
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