Final answer:
The attributes of an error function in training a predictive model pertain to the model's accuracy and precision. They involve evaluating chance error, bias, systematic errors, and prediction discrepancies. The focus is usually on reducing these errors to improve model performance.
Step-by-step explanation:
When training a predictive model, the attributes of an error function typically refer to how well the model performs in predicting the outcomes, usually related to its accuracy and precision. There may be different aspects of these errors, such as chance error and bias. Chance error can happen if the sample size is too small, leaving the sample unrepresentative of the whole population. To mitigate chance error, a larger sample size can be used. Bias may occur when the sampling method isn’t random with respect to a variable of interest; this can be mitigated by careful randomization of the sample selection.
Moreover, the predictive model’s performance is often evaluated using a hypothesis testing approach, where Type I and Type II errors are considered. A Type I error involves incorrectly rejecting a true null hypothesis, while a Type II error involves failing to reject a false null hypothesis. The overall accuracy of a model is dependent on both systematic errors, which affect the trueness of the predictions, and precision, which refers to the consistency of repeated measurements or predictions.
The attribute described by D: The percentage of predictions that don't match actual outcomes is one way to define the performance of an error function. It indicates the discrepancy between the model's predictions and the observed values, thus relating directly to the model's accuracy. Other listed options do not accurately describe the attributes of an error function relevant to predictive models.