DeepSeek addresses class imbalance in its training data through a multi-faceted approach that includes data preprocessing, cost-sensitive learning, and advanced sampling techniques. Class imbalance refers to the scenario where certain classes in a dataset have significantly fewer examples than others. This situation can lead to models that perform poorly, as they may become biased towards the majority class. To mitigate this, DeepSeek first analyzes the distribution of classes in the training data and identifies any imbalances that could affect model performance.
One effective method employed by DeepSeek is data augmentation. This involves creating synthetic samples for the minority classes to bring their representation closer to that of the majority class. For instance, if one class in a dataset represents only 10% of the data, DeepSeek might generate additional examples for that class by applying transformations such as rotation, scaling, and flipping. This way, the model learns from a more balanced set of examples, reducing the risk of overfitting to the majority class. In addition to augmentation, DeepSeek might also implement techniques like oversampling and undersampling, where existing minority class examples are duplicated or majority class examples are randomly removed, respectively.
Another critical strategy used in DeepSeek is cost-sensitive learning. In this approach, different weights are assigned to classes during training to reflect their importance. For instance, misclassifying a minority class example could incur a heavier penalty than misclassifying a majority class example. This adjustment helps guide the model to focus more on learning from underrepresented classes, leading to improved overall performance. By implementing these techniques, DeepSeek effectively manages class imbalance in its training data, enabling the development of more robust and fair models.