👉 Boost math is a technique used in machine learning to enhance the performance of models by amplifying the importance of specific features or data points during training. It works by assigning higher weights to certain inputs, effectively boosting their contribution to the model's predictions. This is often achieved through methods like feature importance scores or by using algorithms that adjust the learning process based on these weights. By focusing more on critical data, boost math helps improve model accuracy and robustness, especially in scenarios where some features are more influential than others. This approach is particularly useful in complex datasets where certain variables significantly impact the outcome, allowing models to learn more effectively and generalize better.
