Commit b5ec7e30 authored by markus's avatar markus
Browse files

Updated readme

parent aee99916
......@@ -45,8 +45,8 @@
<!-- APPROACH -->
## Approach
We decided on using embeddings to represent the carciogenesis dataset in a efficient form. This was done using the PyKeen library, which offers a myriad of different embedding models. Further it can be configured with different parameters like the number of epochs or the dimension of the generated embedding. From this we tried out different approaches for classification ranging from clustering algorithms like KNN to machine learning approaches using the sklearn library. We settled on using random forrests in conjunction with embeddings generated using the \<model_name> model. 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>.
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### Installation of Prerequisites
1. Python > 3.6.9. For example:
1. Python > 3.6.9 For example:
sudo apt install python3.8.10
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