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How can the Focal Loss Function be used to balance data?

asked 2022-10-19 11:00:00 +0000

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answered 2022-11-15 00:00:00 +0000

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The Focal Loss Function is a modification of the standard Cross-Entropy Loss Function. It is designed to give more importance to misclassified examples that are hard to classify. The hard examples are the ones with very low or very high probabilities assigned to them by the model.

When training a model with imbalanced data, the Focal Loss Function can be used to balance the data by giving more weight to the minority class. The loss function gives less weight to easily classified examples and more weight to harder examples, which effectively increases the importance of the minority class during training.

This is achieved by introducing two tunable hyperparameters to the Cross-Entropy Loss Function: alpha controls the class weights and gamma controls the degree to which the loss is focused on hard examples. By setting alpha > 1 and gamma > 0, the Focal Loss Function can be used to balance the data and reduce the impact of the majority class during training.

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Asked: 2022-10-19 11:00:00 +0000

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Last updated: Nov 15 '22