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Thinking about the model performance and/or accuracy of predictions, Selected answer will be automatically saved. For keyboard navigation, press up/down arrow keys to select an answer. a)The model gives correct predictions 97.7% of the time. b)Predictions should be within 95.4% of actual. c)On very few occasions will model predictions be off by more that $1,658 from actual expenses. d)Predictions are usually $829

User Kostia
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Final Answer:

The statement "On very few occasions will model predictions be off by more than $1,658 from actual expenses" indicates an upper limit for prediction errors.

Thus correct option is c)On very few occasions will model predictions be off by more that $1,658 from actual expenses.

Step-by-step explanation:

The statement "On very few occasions will model predictions be off by more than $1,658 from actual expenses" indicates an upper limit for prediction errors. This implies that the model's predictions generally fall within this range but can occasionally exceed it. To put this in perspective, if we consider an error of $1,658 as the maximum deviation, it signifies that most predictions are accurate but a few may be substantially off. This error margin allows for a reasonable understanding of the model's reliability, assuring that extreme outliers are infrequent.

This assertion is crucial in understanding the model's performance as it sets an acceptable limit for the deviation between predicted and actual expenses. For instance, in financial forecasting, such a margin ensures that although the model generally provides reliable predictions, outliers beyond $1,658 should be infrequent occurrences.

If we were to evaluate the performance of the model using this criterion, we would expect the vast majority of predictions to be within this established margin, providing a level of confidence in its accuracy. It's also important to note that while this upper limit sets a threshold for acceptable error, the model's average accuracy or mean absolute error might be significantly lower than this threshold, indicating a higher overall precision in its predictions.

User Alec Matusis
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