In UiPath Communications Mining, over-reliance on the Search feature during the training process can lead to an increase in model bias. This happens because using search-based filtering to identify and label examples might not represent the full diversity of the data. The model could be trained on a skewed subset of the data, causing it to favor certain patterns or keywords, and thus biasing the model towards specific types of data rather than learning to generalize effectively across all data.
Limiting the use of search ensures that the training process considers a broader and more representative sample of the data, which reduces the risk of introducing bias into the model and helps it generalize better to new, unseen communications.
For more details, refer to:
UiPath Communications Mining Documentation: Model Training and Avoiding Bias
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