👉 In the context of computer science and artificial intelligence, pre-handicapping is a process where a machine learning model is trained on a subset of its training data that has already been labeled with positive examples. This allows the model to avoid overfitting by reducing the size of the dataset and focusing on the most relevant features. Pre-handicapping can be useful in situations where the goal is to train a model without having all the data available, or when there are limited resources for training large models.
