Which artifact provides the counts of predictions versus actual outcomes to facilitate error analysis?

Which artifact provides the counts of predictions versus actual outcomes to facilitate error analysis?

• A. Confusion matrix
• B. Hyperparameters
• C. Epochs
• D. Model training parameters

Answer: (A)

A confusion matrix records the actual counts of predictions against what actually happened, organized by predicted and actual classes. It gives you a clear map of how often the model gets each class right or wrong, yielding true positives, false positives, true negatives, and false negatives. This direct tally is exactly what you need for error analysis: you can see which classes are easily confused, identify where the model tends to overpredict or underpredict, and compute metrics like precision, recall, and F1 to gauge performance, especially when classes are imbalanced. Hyperparameters are settings that influence the learning process (like learning rate or regularization) and don’t provide a direct view of prediction outcomes. Epochs refer to how many passes the model makes over the training data, which again isn’t about the predictions vs. actual results. Model training parameters is a broad idea that doesn’t specifically capture the outcome counts either.

A confusion matrix records the actual counts of predictions against what actually happened, organized by predicted and actual classes. It gives you a clear map of how often the model gets each class right or wrong, yielding true positives, false positives, true negatives, and false negatives. This direct tally is exactly what you need for error analysis: you can see which classes are easily confused, identify where the model tends to overpredict or underpredict, and compute metrics like precision, recall, and F1 to gauge performance, especially when classes are imbalanced.

Hyperparameters are settings that influence the learning process (like learning rate or regularization) and don’t provide a direct view of prediction outcomes. Epochs refer to how many passes the model makes over the training data, which again isn’t about the predictions vs. actual results. Model training parameters is a broad idea that doesn’t specifically capture the outcome counts either.