Question

A data science team at a logistics company is preparing a dataset to train a model that predicts package delivery delays. During review, they find that 40% of the historical records are missing the actual delivery timestamp, which is the value the model must learn to predict. Which statement best describes the impact of this issue on the training process?

Accepted Answer

The records missing the delivery timestamp lack a usable label for supervised learning, reducing the amount of high-quality training data available.

Answer

Missing timestamps have no effect because the model can infer them automatically during training.

Answer

The missing values force the team to switch to reinforcement learning, since supervised learning cannot tolerate any incomplete records.

Answer

The issue only matters at inference time, not during training, so the dataset can be used as-is for now.