Intervention
Intervention in data science refers to the process of modifying or manipulating data in order to improve the accuracy or quality of a model's predictions. This can involve a variety of techniques, such as removing outliers, imputing missing values, scaling or transforming variables, or creating new features through feature engineering. The goal of intervention is to optimize the performance of a model by addressing issues that may be hindering its ability to make accurate predictions. Intervention is a critical step in the data science process, as it can have a significant impact on the final results of a model.
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