Fonction de perte
Une fonction de perte, également appelée fonction de coût or fonction objectif, is a mathematical tool utilisé en apprentissage automatique 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 processus d'optimisation by indicating how much the model needs to adjust its parameters to improve accuracy.
Différents types de des fonctions de perte sont utilisés en fonction de la nature du problème :
- Problèmes de régression : For tasks that predict continuous values, common loss functions include Mean Squared Error (MSE) and Erreur Absolue Moyenne (MAE). MSE computes the average of the squares of the errors, emphasizing larger errors more than smaller ones.
- Problèmes de classification : 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 machines à vecteurs de support.
Choosing the right loss function is critical, as it directly affects the model’s ability to learn and its performance globale. 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.