In the context of data science, what is the purpose of a cost function?

The purpose of a cost function in data science is primarily focused on evaluating the performance of a model rather than just measuring its accuracy. A cost function quantifies the difference between the predicted outcomes generated by a model and the actual outcomes in the dataset. By calculating this difference, often through metrics such as mean squared error, cross-entropy loss, or others, the cost function provides a way to assess how well the model is performing.

In practice, minimizing the value of the cost function is essential during the training process. The model parameters are adjusted to reduce this cost, leading to improved predictions and better overall performance. While evaluating model performance is one aspect of using a cost function, it plays a crucial role in the broader context of optimizing and fine-tuning the model's parameters.

The other choices touch upon concepts related to data science but do not encapsulate the primary function of the cost function. For instance, optimizing model parameters is a result of minimizing the cost function, rather than the function's purpose itself. Summarizing data characteristics is typically done through descriptive statistics or visualizations, and defining target outputs pertains to the nature of the problem being solved rather than the evaluation process associated with a cost function.