Recall that the softmax function is a generalization of logistic regression to multiple dimensions and is used in multinomial logistic regression. Cross-entropy loss is commonly used as the loss function for the models which has softmax output. In case, the predicted probability of the class is near to the class label (0 or 1), the cross-entropy loss will be less. In case, the predicted probability of class is way different than the actual class label (0 or 1), the value of cross-entropy loss is high. The cross-entropy loss function is an optimization function that is used for training classification models which classify the data by predicting the probability (value between 0 and 1) of whether the data belong to one class or another. The cross-entropy loss is used as the optimization objective during training to adjust the model’s parameters. It is commonly used in supervised learning problems with multiple classes, such as in a neural network with softmax activation. The function measures the difference between the predicted probability distribution and the true distribution of the target variables. How does cross-entropy loss help to prevent overfitting?Ĭross-entropy loss, also known as negative log likelihood loss, is a commonly used loss function in machine learning for classification problems. What is the relationship between cross-entropy loss and log-likelihood? How does cross-entropy loss compare to mean squared error loss in terms of performance? What type of problem is cross-entropy loss best suited for? What is the purpose of cross-entropy loss in machine learning? Cross-entropy Loss Explained with Python Example.
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