Intervention Strategies
Intervention strategies refer to the methods and techniques used in data science and artificial intelligence to improve the performance of a model or system. These strategies are implemented when a model is not meeting the desired level of accuracy or when the system is not functioning optimally. Intervention strategies can be broadly classified into two categories: model-based and data-based. Model-based intervention strategies involve modifying the structure or parameters of the model to improve its performance. This can include changing the architecture of the model, adjusting the hyperparameters, or fine-tuning the weights. Data-based intervention strategies involve modifying the input data to improve the performance of the model. This can include data cleaning, data augmentation, or feature engineering. Intervention strategies are an important part of the data science and artificial intelligence workflow, as they allow practitioners to improve the accuracy and efficiency of their models and systems.
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