The data science doctor continues his exploration of techniques used to reduce the likelihood of model overfitting, caused by training a neural network for too many iterations. Regularization is a ...

Model fit can be assessed using the difference between the model's predictions and new data (prediction error—our focus ... have low bias and high variance (overfitting). Figure 1: Overfitting ...

Balancing measurement errors with model errors enhances model predictions. Carl Friedrich Gauss, a German mathematician and physicist, made two major changes to a model that his predecessors had tried ...