Description: Self-training is an approach in the field of machine learning where a model is trained using its own predictions as input data. This method is based on the idea that a model can iteratively improve its performance by learning from its mistakes and successes. In the context of machine learning, self-training becomes a powerful tool as it allows models to enhance their performance in a decentralized manner without the need to share sensitive data. This approach is particularly relevant in situations where data privacy and security are paramount. Through self-training, models can adapt to new conditions and enhance their accuracy, resulting in more robust and efficient performance. Additionally, this method can be used to address data scarcity issues, as it allows a model to generate additional data from its own predictions, thereby enriching the training set. In summary, self-training is an innovative technique that enhances machine learning, facilitating the continuous improvement of models through a constant feedback loop.
