Similarly, Utimula et al. used an autoencoder architecture to extract features to aide in phase fraction prediction. A useful feature of unsupervised learning models is that they quantify error according to how much of the original information in the dataset is retained after encoding. For example, Banko et al. showed that the autoencoder reconstruction error (how closely f(x) matches x after encoding) can be used as a proxy for model certainty which decreases the chance of "confidently false" predictions. Reconstruction error can be computed for PCA or any other dimensionality reduction technique, making these powerful complementary techniques to the previously discussed supervised methods.