The top teams, based on the median F1-micro score from 100 realizations of their models were:
|1||LA_Team (Mosser, de la Fuente)||0.6388||Boosted trees||Python||Notebook|
|2||PA Team (PetroAnalytix)||0.6250||Boosted trees||Python||Notebook|
|3||ispl (Bestagini, Tuparo, Lipari)||0.6231||Boosted trees||Python||Notebook|
|4||esaTeam (Earth Analytics)||0.6225||Boosted trees||Python||Notebook|
I have stochastic scores for other teams, and will continue to work through them, but it seems unlikely that these top teams will change at this point.
Welcome to the Geophysical Tutorial Machine Learning Contest 2016! Read all about the contest in the October 2016 issue of the magazine. Look for Brendon Hall's tutorial on lithology prediction with machine learning.
You can run the notebooks in this repo in the cloud, just click the badge below:
You can also clone or download this repo with the green button above, or just read the documents:
F1 scores of models against secret blind data in the STUART and CRAWFORD wells. The logs for those wells are available in the repo, but contestants do not have access to the facies.
** These are deterministic scores, the final standings depend on stochastic scores — see above **
|LA_Team (Mosser, de la Fuente)||0.641||Boosted trees||Python||Notebook|
|ispl (Bestagini, Tuparo, Lipari)||0.640||Boosted trees||Python||Notebook|
|PA Team||0.623||Boosted trees||Python||Notebook|
|Bird Team||0.598||Random forest||Python||Notebook|
|gganssle||0.561||Deep neural net||Lua||Notebook|
|JJlowe||0.556||Deep neural network||Python||Notebook|
|BGC_Team||0.519||Deep neural network||Python||Notebook|
|CannedGeo||0.512||Support vector machine||Python||Notebook|
|ARANZGeo||0.511||Deep nerual network||Python||Code|
|BrendonHall||0.427||Support vector machine||Python||Initial score in article|
Please refer to the User guide to the geophysical tutorials for tips on getting started in Python and find out more about Jupyter notebooks.
We've never done anything like this before, so there's a good chance these rules will become clearer as we go. We aim to be fair at all times, and reserve the right to make judgment calls for dealing with unforeseen circumstances.
IMPORTANT: When this contest was first published, we asked you to hold the SHANKLE well blind. This is no longer necessary. You can use all the published wells in your training. Related: I am removing the file of predicted facies for the STUART and CRAWFORD wells, to reduce confusion — they are not actual facies, only those predicted by Brendon's first model.
Please note that the dataset is not openly licensed. We are working on this, but for now please treat it as proprietary. It is shared here exclusively for use on this problem, in this contest. We hope to have news about this in early 2017, if not before.
All code is the property of its author and subject to the terms of their choosing. If in doubt — ask them.
The information about the contest, and the original article, and everything in this repo published under the auspices of SEG, is licensed CC-BY and OK to use with attribution.