Python package for concise, transparent, and accurate predictive modeling. All sklearn-compatible and easy to use.

docs imodels overview demo notebooks

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 imodels import BoostedRulesClassifier, BayesianRuleListClassifier, GreedyRuleListClassifier, SkopeRulesClassifier # see more models below
from imodels import SLIMRegressor, RuleFitRegressor
model = BoostedRulesClassifier() # initialize a model
model.fit(X_train, y_train) # 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 rule-based model
-----------------------------
# the model consists of the following 3 rules
# if X1 > 5: then 80.5% risk
# else if X2 > 5: then 40% risk
# else: 10% risk
```

Install with `pip install imodels`

(see here for help).

Docs , Reference code implementation , Research paper

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. In particular, many models differ in the 3 steps given by the table below.

See the docs for individual models for futher descriptions.

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

The code here contains many useful and customizable functions for rule-based learning in the util folder. This includes functions / classes for rule deduplication, rule screening, and converting between trees, rulesets, and neural networks.

Demos are contained in the notebooks folder.

`imodels`

for deriving a clinical decision rule
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 | ||

Iterative random forest | IRFClassifier | Requires irf | |

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

Sapling Sums (SAPS) | SaplingSumClassifier | SaplingSumRegressor | |

Shrunk trees | ShrunkTreeClassifierCV | ShrunkTreeRegressorCV | Wraps any sklearn tree-based model |

- 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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