What concept refers to the challenges faced due to a poorly representative training dataset?

The concept that refers to the challenges faced due to a poorly representative training dataset is acquisition bias. This type of bias occurs when the data collected for training a model does not accurately reflect the real-world scenarios in which the model will be applied. If the dataset is skewed or lacks diversity, the model may struggle to generalize and perform well on new, unseen data, leading to poor outcomes or misinterpretations.

Acquisition bias highlights the importance of the data collection process, emphasizing that data should be representative of the various conditions and scenarios the model will encounter. When training datasets are not carefully curated, the risk of the model making incorrect predictions increases, as it may learn patterns that are not applicable outside the limited scope of the training data. This can manifest in various real-world applications, such as biased results in facial recognition or misjudgments in loan approval algorithms, all stemming from the initial data acquisition phase.

In contrast, other options describe different challenges or biases but do not specifically address the central issue of how a non-representative dataset impacts model training to the same degree as acquisition bias does.