Why is the lower bound of the confidence interval of a model's error relatively constant compared to the upper bound? I am interested in studying the effect of increasing data samples for a regression model on train error and test error. For this I have used 95% confidence intervals for different values of a sample data. I found something that i couldn't understand and couldn't find an explanation by looking up in the internet : The lower bound of the confidence interval of the test error stays constant by increasing the number of samples.
The x-axis is the number of samples and the y-axis is the error
The blue line is the lower bound of the confidence interval and the green one is the higher bound


I'm squinting at the plot  a little bit, but it appears that the lower bound is relatively constant compared to the upper bound, but maybe it isn't exactly constant. Here are a couple of separate options to check this further:

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*Try using a log scale on the vertical axis.

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*matplotlib.pyplot.yscale('log')



*Try plotting in different panels with shared x-axis.

fig, axes = plt.subplots(2,1, sharex=True)
axes[0].plot(x, upper_bound_y)
axes[1].plot(x, lower_bound_y)

The above are leads for figuring out whether the lower bound is truly constant, or just relatively constant compared to the changes happening in the upper bound.
I imagine that you might like to know why the lower bound is at least relatively constant. Unfortunately I do not know precisely. I suspect it has to do with

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*the model already having been optimized to minimize error that leads to this asymmetry

*combined with some bound on the predictive accuracy with the given model and data.

