👉 Boot math, short for bootstrapping, is a method used in machine learning to train models when labeled data is scarce or unavailable. It involves iteratively improving the model's performance by using its own predictions to generate additional training data, which is then used to refine the model further. This process starts with a small set of labeled examples and progressively expands the training dataset by generating synthetic data through techniques like bootstrapping or data augmentation. As the model makes predictions on this expanded dataset, it learns from its errors and adjusts its parameters to minimize these mistakes. Over time, this cycle of prediction, error correction, and data expansion helps the model generalize better to unseen data, even with limited initial labeled examples. Bootstrapping is particularly useful in scenarios where collecting more labeled data is impractical or costly, such as in specialized domains or rare event prediction.
