A key challenge for cognitive neuroscience is deciphering the representational schemes of the brain. Stimulus-feature-based encoding models are becoming increasingly popular for inferring the dimensions of neural representational spaces from stimulus-feature spaces. We argue that such inferences are not always valid because successful prediction can occur even if the two representational spaces use different, but correlated, representational schemes. We support this claim with three simulations in which we achieved high prediction accuracy despite systematic differences in the geometries and dimensions of the underlying representations. Detailed analysis of the encoding models' predictions showed systematic deviations from ground-truth, indicating that high prediction accuracy is insufficient for making representational inferences. This fallacy applies to the prediction of actual neural patterns from stimulus-feature spaces and we urge caution in inferring the nature of the neural code from such methods. We discuss ways to overcome these inferential limitations, including model comparison, absolute model performance, visualization techniques and attentional modulation.