A confusion matrix is a fundamental evaluation metric in machine learning, used for assessing the performance of classification algorithms. It is a table that summarizes the number of correct and incorrect predictions made by a model, broken down by class. Statement 1 is correct because the confusion matrix is used for evaluation, and Statement 2 is also correct because it records the predicted vs actual outcomes, providing insights into model performance.
| Case No. | Lens | Focal Length | Object Distance |
|---|---|---|---|
| 1 | \(A\) | 50 cm | 25 cm |
| 2 | B | 20 cm | 60 cm |
| 3 | C | 15 cm | 30 cm |