How Does Bootstrapping Enhance Machine Learning in Cancer Research?
Bootstrapping is integral to many machine learning techniques, such as bagging (bootstrap aggregating), which improves the accuracy and stability of models. In cancer research, machine learning models are often used to predict disease outcomes, classify tumor types, or identify potential therapeutic targets. By using bootstrapping, researchers can reduce overfitting and provide more accurate predictions, which is essential for developing reliable diagnostic tools and treatments.
