Why should 'Labelling using Search Terms' be used sparingly?

Using 'Labelling using Search Terms' should be done cautiously primarily because it may introduce labelling bias. Labelling bias occurs when the search terms employed to classify or label data are influenced by subjective perspectives or assumptions, rather than objective criteria. This can skew the training data, leading to machine learning models that misinterpret inputs, potentially resulting in poor performance in real-world applications. If particular terms are favored over others, the model could overlook or misrepresent nuances in the data, which could mislead its conclusions or predictions.

The other aspects, like data duplication or complications in the labeling process, while relevant, do not reflect the main concern regarding the integrity and objectivity of the labelled data. Similarly, while it is ideal for labelling methods to yield accurate results, inaccuracies are primarily a consequence of bias rather than a direct flaw of the method itself. Hence, understanding and mitigating labelling bias is crucial to ensuring the effectiveness and reliability of machine learning models.