Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Updated paper info #2

Merged
merged 1 commit into from
Sep 27, 2019
Merged

Updated paper info #2

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
8 changes: 6 additions & 2 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,11 @@ pyIDS is a custom implementation of IDS (Interpretable Decision Sets) algorithm

```LAKKARAJU, Himabindu; BACH, Stephen H.; LESKOVEC, Jure. Interpretable decision sets: A joint framework for description and prediction. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. ACM, 2016. p. 1675-1684.```

If you find this package useful in your research, please cite [our article](https://nb.vse.cz/~klit01/papers/RuleML_Challenge_IDS.pdf).
If you find this package useful in your research, please cite our paper on this [Interpretable Decision Sets Implementation](https://nb.vse.cz/~klit01/papers/RuleML_Challenge_IDS.pdf):

Jiri Filip, Tomas Kliegr. PyIDS - Python Implementation of Interpretable Decision Sets Algorithm by Lakkaraju et al, 2016. RuleML+RR2019@Rule Challenge 2019. http://ceur-ws.org/Vol-2438/paper8.pdf



# Examples

Expand Down Expand Up @@ -118,4 +122,4 @@ dataframes = [ pd.read_csv("./data/iris{}.csv".format(i)) for i in range(10)]

kfold = KFoldCV(IDS(), dataframes)
scores = kfold.fit(rule_cutoff=50)
```
```