As visible in Fig. , top left, the model performs better for a higher number of parameters for a given dataset size, but the effect levels off, so that adding more parameters indefinitely is not advisable. A comparison of the bias errors shown in Fig. , middle left pannel, shows that increasing the number of parameters decreases the absolute magnitude of the error from model bias. A steady improvement in bias error appears to be present across all dataset sizes as the model hidden size is increased from 50 to 1000, though further improvement with increases to hidden sizes of 1500 and 2000 are not readily apparent. A too small model (for example h=20) therefore contributes to model bias and should be avoided.