Description: The objective function is a fundamental component in the training of machine learning models, as it represents the metric that is sought to be optimized during the learning process. In simple terms, it is a mathematical function that evaluates the model’s performance in relation to the input data and the predictions made. Its purpose is to guide the adjustment of the model’s parameters, allowing it to learn to make more accurate predictions. Depending on the type of problem, the objective function can take different forms; for example, in classification problems, cross-entropy may be used, while in regression problems, mean squared error is commonly employed. The choice of the objective function is crucial, as it directly influences the quality of the final model. Additionally, in the context of reinforcement learning, the objective function may relate to maximizing cumulative rewards over time. In summary, the objective function acts as a guiding principle in the learning process, enabling models to adjust and improve their performance as they are trained with more data.
