<commit_before># 2016-ml-contest

Welcome to the *Geophysical Tutorial* Machine Learning Contest 2016! Read all about the contest in [the October 2016 issue](http://library.seg.org/toc/leedff/35/10) of the magazine. Look for <NAME>'s tutorial on lithology prediction with machine learning.

**You can run the notebooks in this repo in the cloud, just click the badge below:**

[![Binder](http://mybinder.org/badge.svg)](http://mybinder.org:/repo/seg/2016-ml-contest)

You can also clone or download this repo with the green button above, or just read the documents:

- [index.ipynb](index.ipynb) &mdash; All about the contest.
- [Facies_classification.ipynb](Facies_classification.ipynb) &mdash; Brendon's notebook with all the code you need to get started in machine learning in Python.


## Leaderboard

F1 scores of models against secret blind data in the STUART and CRAWFORD wells. The logs for those wells are available [in the repo](https://github.com/seg/2016-ml-contest/blob/master/validation_data_nofacies.csv), but contestants do not have access to the facies.

|   | Team                                          | F1         | Algorithm     | Language | Solution                 |
|:-:|-----------------------------------------------|:----------:|---------------|----------|--------------------------|
| 1 | geoLEARN                                       | **0.594**  | Random forest | Python | [Notebook](geoLEARN/Submission_3_RF_FE.ipynb) |
| 2 | [gccrowther](https://github.com/gccrowther)    | **0.580**  | Random forest | Python   | [Notebook](GCC_FaciesClassification/01%20-%20Facies%20Classification%20-%20GCC-VALIDATION.ipynb) |
| 3 | MandMs                                         | **0.579**  | Majority voting | Python | [Notebook](MandMs/Facies_classification-M%26Ms_plurality_voting_classifier.ipynb) |
| 4 | LA_Team                                        | **0.574**<sup>1</sup>  | DNN           | Python   | No notebook yet |
| 5 | HouMath                                        | **0.564**  | Boosted trees | Python   | [Notebook](HouMath/Face_classification_HouMath_XGB_02.ipynb) |<commit_msg>Reverting LA Team score to previous best<commit_after># 2016-ml-contest

Welcome to the *Geophysical Tutorial* Machine Learning Contest 2016! Read all about the contest in [the October 2016 issue](http://library.seg.org/toc/leedff/35/10) of the magazine. Look for <NAME>'s tutorial on lithology prediction with machine learning.

**You can run the notebooks in this repo in the cloud, just click the badge below:**

[![Binder](http://mybinder.org/badge.svg)](http://mybinder.org:/repo/seg/2016-ml-contest)

You can also clone or download this repo with the green button above, or just read the documents:

- [index.ipynb](index.ipynb) &mdash; All about the contest.
- [Facies_classification.ipynb](Facies_classification.ipynb) &mdash; Brendon's notebook with all the code you need to get started in machine learning in Python.


## Leaderboard

F1 scores of models against secret blind data in the STUART and CRAWFORD wells. The logs for those wells are available [in the repo](https://github.com/seg/2016-ml-contest/blob/master/validation_data_nofacies.csv), but contestants do not have access to the facies.

|   | Team                                          | F1         | Algorithm     | Language | Solution                 |
|:-:|-----------------------------------------------|:----------:|---------------|----------|--------------------------|
| 1 | geoLEARN                                       | **0.594**  | Random forest | Python | [Notebook](geoLEARN/Submission_3_RF_FE.ipynb) |
| 2 | [gccrowther](https://github.com/gccrowther)    | **0.580**  | Random forest | Python   | [Notebook](GCC_FaciesClassification/01%20-%20Facies%20Classification%20-%20GCC-VALIDATION.ipynb) |
| 3 | MandMs                                         | **0.579**  | Majority voting | Python | [Notebook](MandMs/Facies_classification-M%26Ms_plurality_voting_classifier.ipynb) |
| 4 | LA_Team                                       | **0.568**  | DNN           | Python   | [Notebook](LA_Team/Facies_classification_LA_TEAM_03.ipynb) |
| 5 | HouMath                                        | **0.564**  | Boosted trees | Python   | [Notebook](HouMath/Face_classification_HouMath_XGB_02.ipynb) |
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