Which of the following is a key goal of model tuning?

Enhancing model accuracy and performance is a fundamental goal of model tuning. This process involves adjusting the hyperparameters or configurations of a machine learning model to improve its predictive capabilities and efficiency. By fine-tuning these parameters, practitioners aim to achieve better results on tasks such as classification or regression, leading to more reliable outputs in real-world applications.

Model tuning is crucial because even minor adjustments can significantly impact how well a model generalizes to unseen data. This refinement process helps in reducing overfitting or underfitting, ensuring that the model not only performs well on training data but also maintains its effectiveness when exposed to new data.

Other options do not address the primary objective of model tuning directly. While creating multiple classes for classification relates to the design of a model rather than tuning it, limiting model usage or ensuring independence from data are not typical aims within the tuning phase. Instead, tuning focuses specifically on optimizing performance metrics, making the goal of enhancing accuracy and performance the most pertinent choice.