Commit 6b7909b7 authored by Markus Roese's avatar Markus Roese
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Update README.md

parent cbc95d5e
......@@ -68,6 +68,11 @@ Using this technique and a Linear SVM, we were able to at least slightly improve
Using this we could achieve F1-Scores ranging from \<lower_bound> up to \<higher_bound> for the given test lps.
We split the data in to learning and test in a ratio of \<ratio>.
## Other approaches
We tried out severeal different approaches in order to takle the given task of classifiyng entites. These approaches can be found
in the folder "other_approaches" as Jupyter notebooks.
### SKLEARN Clustering
In the notebook "dbscan_clustering.ipynb" we explored the possibility to use clustering algorithms defined in sklearn in order to classify the given entities. Here we choose DBSCAN, as SKLEARN states it working well with inbalanced datasets. Unfortunetly, the approach did not yield good results and was therefore no longer pursuited.
<!-- PREREQISITES -->
### Prerequisites
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