Percent correctly predicted in probit estimation

[Jan Ostrowski]

Dear statalisters,
I checked my probit model by applying the percent correctly predicted method for its goodness-of-fit. Here is what I got:
Probit model for atrisk_only
-------- True --------
Classified | D ~D | Total
-----------+--------------------------+-----------
+ | 1 1 | 2
- | 245 2474 | 2719
-----------+--------------------------+-----------
Total | 246 2475 | 2721

Classified + if predicted Pr(D) >= .5
True D defined as atrisk_only != 0
--------------------------------------------------
Sensitivity Pr( +| D) 0.41%
Specificity Pr( -|~D) 99.96%
Positive predictive value Pr( D| +) 50.00%
Negative predictive value Pr(~D| -) 90.99%
--------------------------------------------------
False + rate for true ~D Pr( +|~D) 0.04%
False - rate for true D Pr( -| D) 99.59%
False + rate for classified + Pr(~D| +) 50.00%
False - rate for classified - Pr( D| -) 9.01%
--------------------------------------------------
Correctly classified 90.96%

It seems that my model is good in predicting when y=0 (what is not a surprise, since in most cases of my sample y=0), but fails to predict when y=1.
Is it correct to take this as a sign that my model does not have a good fit? What could I do to improve my model? Are there other goodness-of-fit measures that might be more suitable for my case?
Thank you in advance!
Best Jan

[Steve Samuels]

Welcome to Statalist, Jan! Unfortunately, you've posted in the Sandbox, whose header states. "Use this forum to make test posts, practice with the formatting features of the forum software, etc. Post in the General Forum, but before you do read the FAQ, especially FAQ 12. In particular, place all commands and results between CODE delimiters, to ensure that columns align and text is readable.

[Jan Ostrowski]

Thank you for the advice, Steve!