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csinva · Sep 13, 2020 · dd4a2f7 · dd4a2f7
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diff --git a/data/boston.csv b/data/boston.csv
diff --git a/imodels/optimal_classification_tree/__init__.py b/imodels/optimal_classification_tree/__init__.py
@@ -1,2 +0,0 @@
-'''(in progress) [optimal classification tree](https://link.springer.com/article/10.1007/s10994-017-5633-9) (based on [this implementation](https://github.com/pan5431333/pyoptree)) - learns succinct trees using global optimization rather than greedy heuristics
-'''

diff --git a/...l_classification_tree/pyoptree/example.py → ...ls/optimal_classification_tree/example.py b/...l_classification_tree/pyoptree/example.py → ...ls/optimal_classification_tree/example.py
diff --git a/...assification_tree/pyoptree/localsearch.py → ...ptimal_classification_tree/localsearch.py b/...assification_tree/pyoptree/localsearch.py → ...ptimal_classification_tree/localsearch.py
diff --git a/...al_classification_tree/pyoptree/optree.py → ...els/optimal_classification_tree/optree.py b/...al_classification_tree/pyoptree/optree.py → ...els/optimal_classification_tree/optree.py
diff --git a/imodels/optimal_classification_tree/pyoptree/__init__.py b/imodels/optimal_classification_tree/pyoptree/__init__.py
diff --git a/...imal_classification_tree/pyoptree/tree.py → imodels/optimal_classification_tree/tree.py b/...imal_classification_tree/pyoptree/tree.py → imodels/optimal_classification_tree/tree.py
diff --git a/imodels/skrules/datasets/__init__.py b/imodels/skrules/datasets/__init__.py
diff --git a/imodels/skrules/datasets/credit_data.py b/imodels/skrules/datasets/credit_data.py
diff --git a/notebooks/1_model_based.ipynb b/notebooks/1_model_based.ipynb
diff --git a/readme.md b/readme.md
@@ -40,7 +40,7 @@ preds_proba = model.predict_proba(X_test) # predicted probabilities: shape is (n
 ## Demo notebooks
 The demos are contained in 3 main [notebooks](notebooks), following this cheat-sheet:![cheat_sheet](docs/cheat_sheet.png)
 
-1. [model_based.ipynb](notebooks/1_model_based.ipynb) - how to use different interpretable models
+1. [model_based.ipynb](notebooks/1_model_based.ipynb) - how to use different interpretable models and examples with the **imodels** package
 2. [posthoc.ipynb](notebooks/2_posthoc.ipynb) - different simple analyses to interpret a trained model
 3. [uncertainty.ipynb](notebooks/3_uncertainty.ipynb) - code to get uncertainty estimates for a model
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		@@ -1,2 +0,0 @@
		'''(in progress) [optimal classification tree](https://link.springer.com/article/10.1007/s10994-017-5633-9) (based on [this implementation](https://github.com/pan5431333/pyoptree)) - learns succinct trees using global optimization rather than greedy heuristics
		'''