Baiting
Baiting is a technique used in data science to manipulate or influence the behavior of a machine learning model by introducing biased or misleading data into the training dataset. This can be done intentionally to achieve a desired outcome, such as increasing the accuracy of the model on a specific subset of data, or to undermine the performance of a competitor's model. Baiting can also occur unintentionally, when the training dataset is not representative of the real-world data that the model will encounter. This can lead to overfitting, where the model performs well on the training data but poorly on new data. Baiting can be mitigated through techniques such as cross-validation, regularization, and outlier detection.
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