Why is Ridge Regularization Important in Cancer Research?
In cancer research, datasets often contain a large number of features (e.g., gene expressions, biomarkers) compared to the number of samples. This high-dimensional data can lead to overfitting, where the model performs well on training data but poorly on test data. Ridge regularization helps mitigate this by shrinking the coefficients, thus reducing the model's variance without substantially increasing its bias. This is essential for developing reliable predictive models for cancer diagnosis, prognosis, and treatment response.
