# How to transform an accuracy distribution for a violin plot

I am trying to find the best way to visualize different distributions of accuracy. Accuracy here is a value in the interval [0,1], 0 meaning not accurate, 1 maximum accuracy.

I have different methods to compare, so I decided to use violin plots.

Distributions are clustered near 1 but they have also a long tail (the first one is cut at 0.45).

How can I transform the data (e.g. in log-scale), in order to visualize better the differences between these plots? I want to focus in the interval [0.8, 1], but I want also to retain the long tails.

I do not want to use boxplots because in this case I saw that they do not show correctly the distributions (also because the lower quartile is already 1).

I add also the corresponding boxplot.

Thanks

• If the first quartile (the lower quartile) is 1, then at least 75% of the distribution is at 1. In that case, no transformation will separate those values at 1, since they all have the same value - they'll always be transformed to the same thing. One thing you can do is look at the ecdf, perhaps. Commented Jun 6, 2014 at 9:34
• You are right..
– gc5
Commented Jun 6, 2014 at 9:36
• If the lower quartile is already 1, then how it is readable from these plots that more than 75% of the values are at the right-hand edge? Either they lie or they are extraordinarily over-smoothed. Commented Jun 6, 2014 at 13:36
• @NickCox I used boxplot.tyerslab.com .. in fact, it is the first time I use violin plots - I do not exactly what to expect
– gc5
Commented Jun 6, 2014 at 17:04
• I don't think the issue requires much expertise. You are telling us that >75% of the values are 1; I can't see that the violin plots tell us that, except that in 3 out of 4 instances there seems to be a minute fragment of a box. Conversely, the other instance seems to show more of a box. So I have no sense that the violin plots are showing the data faithfully. I've already suggested an alternative visualization. Another would be a quantile plot. Commented Jun 8, 2014 at 8:12