Which of the following is NOT a characteristic of a good training dataset?

A good training dataset is essential for training robust and effective machine learning models. Its characteristics play a critical role in determining how well the model performs.

Relevant features ensure that the data being used aligns with the problem the model is trying to solve. If the features are not relevant, the model may end up learning patterns that do not actually correlate with the outputs, leading to poor performance.

Diverse samples allow the model to generalize better to unseen data. A dataset that captures various scenarios and variations within the data helps to make the model more robust against overfitting, as it learns from a broad range of situations.

Sufficient size is crucial because having a larger dataset typically helps improve the model's accuracy and reliability. A small dataset may not provide enough information for the model to learn the underlying patterns, resulting in a model that doesn't perform well in practice.

High noise levels in a dataset can significantly hinder a model's learning process. When there is too much noise—random variability that does not contain useful information—it can obscure the true signals in the data, making it difficult for the model to learn effectively. Therefore, a good training dataset should aim for low noise levels to help ensure that the model learns the important patterns rather than fitting to irrelevant fluctuations.