Figure 4.

Schematic plots showing underfit (left) and overfit (right) models compared with the best fit (centre). The same data points are plotted (a quadratic function with random noise). The left panel shows a linear model, the right panel shows a high-order polynomial fitted to the data, the centre panel shows the quadratic function which was used to derive the data points. The model on the right panel is highly tuned to random noise in the data, and so is likely to perform poorly at predicting Y values from X values in an independent dataset. The linear model fits these data less ‘tightly’ (ie, there is a higher overall error), and so is less likely to predict Y values based on random noise but may be underfitted in that it does not capture some important structure in the data. The centre panel shows a quadratic function which captures the ‘true’ underlying distribution of the data and so is likely to perform best in an independent dataset.