 This estimate is deemed a model’s ‘Akaike Information Criterion’ (AIC) score—it measures goodness of fit in terms of the log likelihood of the data on the assumption of \(L(M)\). The simplicity of the model is inversely proportion to k, the number of adjustable parameters in the model. The intuitive idea is that models with a high k value will provide a large variety of curves that will tend to fit data more closely than models with a lower k value—and thus large k values are more prone to overfitting than small k values. So the AIC score assesses a model’s likely predictive accuracy in a way that balances both goodness of fit and simplicity, and the curve-fitting problem is arguably solved. 