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I'm trying to create a plot of classification accuracy for three ML models, depending on the number of features used from the data (the number of features used is from 1 to 75, ranked according to a feature selection method). I did 100 iterations of calculating the accuracy output for each model and for each "# of features used". Below is what my data looks like (clsf from 0 to 2, timepoint from 1 to 75): data

I am then calling the seaborn function as shown in documentation files. 

sns.lineplot(x= "timepoint", y="acc", hue="clsf", data=ttest_df, ci= "sd", err_style = "band")

The plot comes out like this: plot

I wanted there to be confidence intervals for each point on the x-axis, and don't know why it is not working. I have 100 y values for each x value, so I don't see why it cannot calculate/show it.

Zofia
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1 Answers1

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You could try your data set using Seaborn's pointplot function instead. It's specifically for showing an indication of uncertainty around a scatter plot of points. By default pointplot will connect values by a line. This is fine if the categorical variable is ordinal in nature, but it can be a good idea to remove the line via linestyles = "" for nominal data. (I used join = False in my example)

I tried to recreate your notebook to give a visual, but wasn't able to get the confidence interval in my plot exactly as you describe. I hope this is helpful for you.

enter image description here

sb.set(style="darkgrid")
sb.pointplot(x = 'timepoint', y = 'acc', hue = 'clsf',
            data = ttest_df, ci = 'sd', palette = 'magma', 
            join = False);

enter image description here

jwho
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