The process of making adjustments to improve model performance is known as:

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The process of making adjustments to improve model performance is referred to as parameter tuning. This involves optimizing the hyperparameters of a machine learning model, which are the configurations that affect how the model learns from data. By fine-tuning these parameters, practitioners can significantly enhance the model's accuracy and efficiency. This typically includes techniques such as grid search or random search to explore various combinations of parameters systematically.

Model architecture design relates to creating the structure of the model itself, which involves decisions about how various components of the model are arranged. Data analysis focuses on examining and processing the data used in training the model, but it doesn’t directly involve the performance of the model itself. Lastly, model evaluation is the process of assessing the model’s performance using metrics and validation techniques, but it does not include the proactive adjustments made during parameter tuning. Thus, parameter tuning specifically targets the enhancement of model performance by adjusting the variables that govern learning behavior.

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