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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 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 data samples while the y-axis is the test error

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