Python package for concise, transparent, and accurate predictive modeling.

All sklearn-compatible and easy to use.

* For interpretability in NLP, check out our new package: imodelsX *

Modern machine-learning models are increasingly complex, often making them difficult to interpret. This package provides a simple interface for fitting and using state-of-the-art interpretable models, all compatible with scikit-learn. These models can often replace black-box models (e.g. random forests) with simpler models (e.g. rule lists) while improving interpretability and computational efficiency, all without sacrificing predictive accuracy! Simply import a classifier or regressor and use the `fit`

and `predict`

methods, same as standard scikit-learn models.

```
from sklearn.model_selection import train_test_split
from imodels import get_clean_dataset, HSTreeClassifierCV # import any imodels model here
# prepare data (a sample clinical dataset)
X, y, feature_names = get_clean_dataset('csi_pecarn_pred')
X_train, X_test, y_train, y_test = train_test_split(
X, y, random_state=42)
# fit the model
model = HSTreeClassifierCV(max_leaf_nodes=4) # initialize a tree model and specify only 4 leaf nodes
model.fit(X_train, y_train, feature_names=feature_names) # fit model
preds = model.predict(X_test) # discrete predictions: shape is (n_test, 1)
preds_proba = model.predict_proba(X_test) # predicted probabilities: shape is (n_test, n_classes)
print(model) # print the model
```

```
------------------------------
Decision Tree with Hierarchical Shrinkage
Prediction is made by looking at the value in the appropriate leaf of the tree
------------------------------
|--- FocalNeuroFindings2 <= 0.50
| |--- HighriskDiving <= 0.50
| | |--- Torticollis2 <= 0.50
| | | |--- value: [0.10]
| | |--- Torticollis2 > 0.50
| | | |--- value: [0.30]
| |--- HighriskDiving > 0.50
| | |--- value: [0.68]
|--- FocalNeuroFindings2 > 0.50
| |--- value: [0.42]
```

Install with `pip install imodels`

(see here for help).

Docs , Reference code implementation , Research paper

Demos are contained in the notebooks folder.

`imodels`

for deriving a clinical decision rule
The final form of the above models takes one of the following forms, which aim to be simultaneously simple to understand and highly predictive:

Rule set | Rule list | Rule tree | Algebraic models |
---|---|---|---|

Different models and algorithms vary not only in their final form but also in different choices made during modeling, such as how they generate, select, and postprocess rules:

Rule candidate generation | Rule selection | Rule postprocessing |
---|---|---|

Different models support different machine-learning tasks. Current support for different models is given below (each of these models can be imported directly from imodels (e.g. `from imodels import RuleFitClassifier`

):

Model | Binary classification | Regression | Notes |
---|---|---|---|

Rulefit rule set | RuleFitClassifier | RuleFitRegressor | |

Skope rule set | SkopeRulesClassifier | ||

Boosted rule set | BoostedRulesClassifier | ||

SLIPPER rule set | SlipperClassifier | ||

Bayesian rule set | BayesianRuleSetClassifier | Fails for large problems | |

Optimal rule list (CORELS) | OptimalRuleListClassifier | Requires corels, fails for large problems | |

Bayesian rule list | BayesianRuleListClassifier | ||

Greedy rule list | GreedyRuleListClassifier | ||

OneR rule list | OneRClassifier | ||

Optimal rule tree (GOSDT) | OptimalTreeClassifier | Requires gosdt, fails for large problems | |

Greedy rule tree (CART) | GreedyTreeClassifier | GreedyTreeRegressor | |

C4.5 rule tree | C45TreeClassifier | ||

TAO rule tree | TaoTreeClassifier | TaoTreeRegressor | |

Iterative random forest | IRFClassifier | Requires irf | |

Sparse integer linear model | SLIMClassifier | SLIMRegressor | Requires extra dependencies for speed |

Greedy tree sums (FIGS) | FIGSClassifier | FIGSRegressor | |

Hierarchical shrinkage | HSTreeClassifierCV | HSTreeRegressorCV | Wraps any sklearn tree-based model |

Distillation | DistilledRegressor | Wraps any sklearn-compatible models |

After developing and playing with `imodels`

, we developed a few new models to overcome limitations of existing interpretable models.

Fast Interpretable Greedy-Tree Sums (FIGS) is an algorithm for fitting concise rule-based models. Specifically, FIGS generalizes CART to simultaneously grow a flexible number of trees in a summation. The total number of splits across all the trees can be restricted by a pre-specified threshold, keeping the model interpretable. Experiments across a wide array of real-world datasets show that FIGS achieves state-of-the-art prediction performance when restricted to just a few splits (e.g. less than 20).

**Example FIGS model.** FIGS learns a sum of trees with a flexible number of trees; to make its prediction, it sums the result from each tree.

Paper (ICML 2022), Post, Citation

Hierarchical shrinkage is an extremely fast post-hoc regularization method which works on any decision tree (or tree-based ensemble, such as Random Forest). It does not modify the tree structure, and instead regularizes the tree by shrinking the prediction over each node towards the sample means of its ancestors (using a single regularization parameter). Experiments over a wide variety of datasets show that hierarchical shrinkage substantially increases the predictive performance of individual decision trees and decision-tree ensembles.

**HS Example.** HS applies post-hoc regularization to any decision tree by shrinking each node towards its parent.

- pycorels - by @fingoldin and the original CORELS team
- sklearn-expertsys - by @tmadl and @kenben based on original code by Ben Letham
- rulefit - by @christophM
- skope-rules - by the skope-rules team (including @ngoix, @floriangardin, @datajms, Bibi Ndiaye, Ronan Gautier)
- boa - by @wangtongada

- gplearn: symbolic regression/classification
- pysr: fast symbolic regression
- pygam: generative additive models
- interpretml: boosting-based gam
- h20 ai: gams + glms (and more)
- optbinning: data discretization / scoring models

- For updates, star the repo, see this related repo, or follow @csinva_
- Please make sure to give authors of original methods / base implementations appropriate credit!
- Contributing: pull requests very welcome!

If it's useful for you, please star/cite the package, and make sure to give authors of original methods / base implementations credit:

```
@software{
imodels2021,
title = {imodels: a python package for fitting interpretable models},
journal = {Journal of Open Source Software},
publisher = {The Open Journal},
year = {2021},
author = {Singh, Chandan and Nasseri, Keyan and Tan, Yan Shuo and Tang, Tiffany and Yu, Bin},
volume = {6},
number = {61},
pages = {3192},
doi = {10.21105/joss.03192},
url = {https://doi.org/10.21105/joss.03192},
}
```

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