Função de Perda
Uma função de perda, também conhecida como uma função de custo or função objetivo, is a mathematical tool usada em aprendizado de máquina to evaluate how well a model’s predictions align with actual outcomes. It quantifies the difference between predicted values (outputs) and the true values (targets) for a given dataset.
In essence, the loss function provides a score that indicates the performance of a model: the lower the score, the better the model’s predictions. This score is crucial for training algorithms, as it guides the processo de otimização by indicating how much the model needs to adjust its parameters to improve accuracy.
Diferentes tipos de funções de perda são usados dependendo da natureza do problema:
- Problemas de Regressão: For tasks that predict continuous values, common loss functions include Mean Squared Error (MSE) and Erro Médio Absoluto (MAE). MSE computes the average of the squares of the errors, emphasizing larger errors more than smaller ones.
- Problemas de Classificação: In classification tasks, where the output is a category, loss functions like Cross-Entropy Loss and Hinge Loss are frequently employed. Cross-Entropy Loss measures the dissimilarity between the predicted probability distribution and the actual distribution, while Hinge Loss is often used for Máquinas de Vetores de Suporte.
Choosing the right loss function is critical, as it directly affects the model’s ability to learn and its desempenho geral. In practice, adjustments to the loss function may be necessary to align with specific goals, such as improving robustness against outliers or optimizing for particular metrics.