What are the two main reasons for estimating f in supervised learning?

In supervised learning, estimating the function ( f ) that maps inputs to outputs serves two primary purposes: prediction and inference.

Prediction is essential because the primary goal of supervised learning is to build a model that can accurately predict outcomes based on new input data. The model learns from historical data, identifying patterns and relationships that allow it to generalize and provide accurate predictions for unseen data.

Inference, on the other hand, focuses on understanding the underlying relationships between variables, determining the importance of different predictors, or assessing the potential impact of changes in input variables on the output. This is crucial in many fields, such as economics or social sciences, where understanding causality and relationships is as important as making accurate predictions.

While accuracy and interpretability are significant factors in evaluating a model’s performance, they are not the fundamental reasons for estimating ( f ). Instead, they relate to the quality of the predictions and the clarity with which model outputs can be understood. Thus, the emphasis on prediction and inference highlights the core objectives of supervised learning.