Why is Regularization Important in Cancer Studies?
In cancer studies, the datasets are often complex and noisy, making it challenging to identify significant biomarkers or to predict outcomes accurately. Regularization techniques help in mitigating the risk of overfitting, where a model performs well on the training data but fails to generalize to new, unseen data. This is crucial for developing reliable diagnostic tools and treatment plans.
