Final answer:
Accuracy on holdout data measures the performance of a predictive model on new, unseen data and is sometimes called validation accuracy or test accuracy. It reflects the trueness of the model's predictions and is distinct from precision, which measures the repeatability of predictions.
Step-by-step explanation:
Accuracy on holdout data refers to the performance of a predictive model on a set of data that was not used during the model's training phase. This measure is crucial to understand how well the model generalizes to new, unseen data. It is sometimes called validation accuracy or test accuracy depending on the context and the stage at which the holdout data is used.
In the context of machine learning, accuracy is a measure of the trueness of the model's predictions, meaning how often the predictions match the actual values. Trueness relates to the absence of systematic errors, which can bias the predictions of the model. Therefore, less systematic error results in a more accurate average. On the other hand, precision represents how consistent or repeatable the predictions are. In other words, precision assesses if repeated measurements under unchanged conditions produce the same results.
Assessing the accuracy of a model on holdout data helps to ensure that machine learning algorithms are not just memorizing the training data but actually learning patterns that can be applicable to new data. This practice helps in avoiding models that are overfit to the training set and thus, potentially inaccurate when applied to real-world situations.