El Cruz Entropía Objetivo is a widely used función de pérdida in aprendizaje automático, particularly in the context of classification tasks. It quantifies the difference between two distribuciones de probabilidad: the true distribution of labels and the predicted distribution output by a model. The objective is to minimize this difference, which represents how well the model’s predictions align with the actual labels.
Matemáticamente, la entropía cruzada se define como:
H(p, q) = -Σ p(x) log(q(x))
donde:
- H(p, q) is the cross entropy between the true distribution p and the predicted distribution q.
- p(x) is the true probability of class labels (usually represented as one-hot encoded vectors).
- q(x) es la probabilidad predicha de las etiquetas de clase que produce el modelo.
In practical terms, when using cross entropy as the objective function, the model is penalized more heavily for confident but incorrect predictions. This characteristic makes it especially effective for tasks where accurate probability estimation is critical, such as in clasificación multiclase problemas.
Cross entropy is commonly employed in various machine learning frameworks and is particularly effective with neural networks when combined with softmax funciones de activación in the output layer. The optimization process adjusts the model parameters to minimize the cross entropy loss, thereby improving the model’s accuracy and reliability in predicting outcomes.