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# 2020_data_mining_assignments
-[link to assignment](http://www.cs.uu.nl/docs/vakken/mdm/assignment1-2020.pdf)
+* [link to assignment](http://www.cs.uu.nl/docs/vakken/mdm/assignment1-2020.pdf)
+* [link to article datasets part 2](https://www.st.cs.uni-saarland.de/softevo/bug-data/eclipse/promise2007-dataset-20a.pdf)
## Part1, tree algorithm/implementation:
- [X] tree_grow functie die het [pseudocode in de slides](./media/tree_grow_pseudo_code.png) volgt
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- [X] [tree_pred functie](https://github.com/Vinkage/2020_data_mining_assignments/blob/da8ca975fb9d11d3801fef66344736e675734c42/assignment1.py#L77-L103) met efficiente conditional branches
- [ ] tree_pred_b een functie die een lijst van tree kan gebruiken om een voorspelling te maken voor rows in een data array x
+- [ ] Figure out how we want to compute the confusion matrix (scipy?)
+- [ ] Test prediction of single tree on pima indians data with nmin 20 and minleaf 5, check with confusion matrix in [link to assignment](http://www.cs.uu.nl/docs/vakken/mdm/assignment1-2020.pdf)
+
## Part2, data analysis:
- [ ] Datasets collecten uit de literature
- [ ] Datasets describen, exploren/plotten/formatten als het nodig is
-- [ ] Figure out how to compute accuracy, precision, recall for predictions in python
-- [ ] Figure out how we want to compute the confusion matrix (scipy?)
### Official steps
![](./media/steps_data_anal.png)