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I am confused about how to evaluate this result. From this link, it seems like my model is just right, I just want to make sure that my result is a good fit. Any helps would be appreciated. Thanks!

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zoom: enter image description here

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  • $\begingroup$ What are the units for error? $\endgroup$ – Michael R. Chernick May 5 '17 at 20:36
  • $\begingroup$ The unit for error is the amount of the links utilization. Please correct me if something's wrong here. Thanks, Mike! $\endgroup$ – Tung Le May 5 '17 at 21:11
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I have an answer on how to read learning curve here. I think it may be helpful.

How to know if a learning curve from SVM model suffers from bias or variance?

I would suggest you to use more data, instead of 1 to 27 data points. The reason is in your plot, the numbers are easily get very small after passing $n=3$ and the $y$ axis is in a larger scale.

In sum,

  • Try to use more data
  • Try to make y axis scale smaller to clearly see two lines.
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  • $\begingroup$ Thanks, hxd1011. From what I've learned from ML, there is a high bias (underfit) when J_{train}{\theta} is high and J_{cross-validate}(\theta) ~ J_{train}{\theta}, so getting more data will not help much. There is a high variance (overfit) when J_{train}(\theta) is low and J_{cross-validate}(\theta) >> J_{train}{\theta}, so getting more data is likely to help. But in my case, J_{train}(\theta) is low, J_{cross-validate}(\theta)~J_{train}(\theta). That's why I am confused about this situation. As the data are pretty scarce in my case, are there alternatives to solve it without using more data? $\endgroup$ – Tung Le May 5 '17 at 21:19

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