balanced crossentropy
时间: 2023-12-14 20:04:18 浏览: 39
Balanced cross-entropy is a modified version of the standard cross-entropy loss function used in machine learning. The standard cross-entropy loss function is commonly used in binary classification tasks where the output is either 0 or 1. However, in cases where the dataset is imbalanced, meaning one class has significantly more samples than the other, the standard cross-entropy loss function can result in biased models.
Balanced cross-entropy attempts to address this issue by introducing a weighting factor to account for the class imbalance. The weighting factor is calculated as the inverse of the class frequency, meaning that the less frequent class is given a higher weight. This weighting encourages the model to pay more attention to the less frequent class, resulting in a more balanced model.
The balanced cross-entropy loss function is given by:
L(y, y') = -αylog(y') - (1 - α)(1 - y)log(1 - y')
where y is the true label (0 or 1), y' is the predicted probability of the positive class, and α is the weighting factor calculated as:
α = n / (2m)
where n is the total number of samples and m is the number of positive samples.
Overall, the use of balanced cross-entropy can improve the performance of models in imbalanced datasets, leading to better predictive accuracy and more equitable results.