The test set and cross-validation assessments write to result files in the model directory as described in section 2.2.3. These result files are human readable and can be used to easily generate any visualization users require. QSPRpred also has a ModelPlot class, that provides a number of different plots that can be generated directly from an instantiated model with result files present. These plots include receiver operating characteristic (ROC) curves, precision-recall curves, calibration plot and barplots for a range of scikit-learn classification metrics. Metrics such as precision, recall and Matthews Correlation Coefficient can be visualized not only for single-task models, but also for multi-task and multi-class classification models. In the case of multi-class models metrics are calculated per class (one-vs-rest) and with different averages. Moreover, correlation plots can be generated for multi and single-task regression models. In addition to the native ModelPlot, QSPRpred's MoleculeTable and QSPRModel instances can be used directly with the interactive chemoinformatics visualization package Scaffviz . Scaffviz offers alternative visualization of model errors and is essentially an adapter between molplotly and QSPRpred. It can be used to apply dimensionality reduction methods to obtain 2D embeddings of molecules from descriptors and display them in an interactive scatter plot. However, any properties from the data set can be displayed on each axis as well. Points may be coloured by any property in the data set, including training and test splits. It can also visualize model errors for mispredicted compounds as color overlay as well, which helps identifying difficult compounds to predict.