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I am a big fan of violin-plots. Although both aim for the same goal (visualizing distributions and key figures for that), box-plots have their limitations. Please have a look into following gif [1]): Box-plots are not able to capture the change in the raw data, voilin-plots do and are able to do so:
[1] taken from https://www.autodesk.com/research/...
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Ignoring for a moment the variance of the first feature, a straightforward approach is to perform a linear combination of the features $x_1$, $x_2$ and $x_3$, with each of the coefficients being a hyper-parameter you set to indicate the relative importance of that feature (perhaps normalizing the features before hand). This will be your molecule's utility ...
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PCA removes the connection with the original features,so the interpretation of the visualisations in the principle component space is therefore not very meaningful.
E.g. cluster A has higher values of PC1, where cluster B has higher values of PC2.
If you can clearly see that PC1 is only representative of Feature X, then fine, but this isn't often the case.
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