👉 Favor engineering is an advanced technique in machine learning and deep learning that involves creating and optimizing artificial "favorites" or positive examples to guide the training of models, particularly in scenarios where labeled data is scarce or expensive to obtain. By selectively enhancing or augmenting these favorable examples, the model can learn more effectively and efficiently, improving its performance on tasks such as classification or regression. This approach often involves techniques like data augmentation, where positive examples are artificially generated to balance the dataset or create more diverse training samples, leading to better generalization and robustness of the model. Favor engineering can be especially useful in fine-tuning pre-trained models for specific tasks, allowing them to adapt more quickly and effectively to new challenges with minimal additional labeled data.
