WorldWar33.3
Well-Known Member
At first, I thought I said something rude or hurt someone's feelings way back when... I'm glad it wasn't me being a douchenozzle
Do you know if active duty people, already did meps to enlist, have to redo it?Looks like we got some more entries on the google sheet in that time though, so people are starting to trickle in. I managed to get all my paperwork done so now all I should have left to do for my application is go to MEPS.
Not a single clue man. I would assume you've already got all your medical stuff in whatever database the navy is using, but idk.Do you know if active duty people, already did meps to enlist, have to redo it?
Thanks anyways man. Good job getting all your paperwork done.Not a single clue man. I would assume you've already got all your medical stuff in whatever database the navy is using, but idk.
You would need to do a commissioning physical through your medical, the info should be in the 1420Do you know if active duty people, already did meps to enlist, have to redo it?
Thank you. I will read into it to see if that is something different than a 2808 or 2807 form.You would need to do a commissioning physical through your medical, the info should be in the 1420
It would be on the same forms but needs to be a commissioning physical which includes a few extras, and it needs to be within a year, maybe 2 in order to be accepted.Thank you. I will read into it to see if that is something different than a 2808 or 2807 form.
I'd definitely be interested in testing out my scores! They're underneath my posts. My low GPA is what worries most. Do you need more info?Hey y’all, I had too much time on my hands so I tried making an ML model of predicting SNA selection this afternoon.
It currently predicts Y/N with a 93% accuracy and I can try your scores through it if you want.
A couple of huge caveats here with the data:
So with those rather large caveats out of the way, here are some observations:
- The quality of training data is pretty shit. I could only find 3ish spreadsheets easily on the AW forums, and was too lazy to go and find others.
- The “waivers” column seems to be a relatively recent addition, so it doesn’t really exist in the previous data. That said, accuracy didn’t improve when I discarded
- There is a pretty big bias towards “yes” because only the die-hards pilot wannabes post on this forum/spreadsheets and those that do are generally pretty likely to get yes’s. There is a surprising lack of prorec-N’s in the sheets I looked at.
- The selection yes/no is purely based off of SNA applicants. I didn’t include NFO because that was too much work and it seems you’re pretty likely to get in if you select NFO as your primary.
- I only had 150ish rows of data from the sheets I found, so I duplicated it on the assumption that the spread of applicant stats is relatively similar across boards, in order to get more training data. So, the existing biases are doubled.
- Didn’t include college major b/c I didnt' want to deal with it.
- This is strictly from the spreadsheet data, so it’s not reflecting the “whole person” concept with stuff that can’t be quantified. <- biggest caveat
Next steps: if any of you guys and gals have extra time, it would be interesting to build out a better dataset together. That means finding the past board spreadsheets, cleaning it, and putting it into this model to train it better. That would yield better results, and I might be able to make a webapp so ppl can look up their own scores.
- Age IS unsurprising a big factor. Curiously, there’s a dip between 27-29, but I think that’s mostly a dataset issue.
- PFAR is the next best predictor
- OAR and GPA are weighted pretty similarly, as is AQR
- Interestingly, the FOFAR is a better predictor than Flight experience - meaning it can be said with pretty good confidence that flight exp doesn’t really matter.
- Prior service and sex are also negligible.
Using the model, I tried predicting my own prob of prorec-Y and got a Yes with 96%. LMK if you want me to try putting your scores into it and seeing what comes out. Caveats blah blah blah.
The model itself:
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Trying my own scores
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