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Expand Up @@ -64,36 +64,36 @@ Install with `pip install imodels` (see [here](https://github.com/csinva/imodels

### Supported models

<p align="left">
<a href="https://csinva.io/imodels/">🗂️</a> Docs &emsp; 📄 Research paper &emsp; 🔗 Reference code implementation
</br>
</p>

| Model | Reference | Description |
| :-------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
| Rulefit rule set | [🗂️](https://csinva.io/imodels/rule_set/rule_fit.html), [🔗](https://github.com/christophM/rulefit), [📄](http://statweb.stanford.edu/~jhf/ftp/RuleFit.pdf) | Fits a sparse linear model on rules extracted from decision trees |
| Rulefit rule set | [🗂️](https://csinva.io/imodels/rule_set/rule_fit.html), [📄](http://statweb.stanford.edu/~jhf/ftp/RuleFit.pdf), [🔗](https://github.com/christophM/rulefit) | Fits a sparse linear model on rules extracted from decision trees |
| Skope rule set | [🗂️](https://csinva.io/imodels/rule_set/skope_rules.html#imodels.rule_set.skope_rules.SkopeRulesClassifier), [🔗](https://github.com/scikit-learn-contrib/skope-rules) | Extracts rules from gradient-boosted trees, deduplicates them,<br/>then linearly combines them based on their OOB precision |
| Boosted rule set | [🗂️](https://csinva.io/imodels/rule_set/boosted_rules.html), [🔗](https://github.com/jaimeps/adaboost-implementation), [📄](https://www.sciencedirect.com/science/article/pii/S002200009791504X) | Sequentially fits a set of rules with Adaboost |
| Slipper rule set | [🗂️](https://csinva.io/imodels/rule_set/slipper.html), &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;, ㅤㅤ[📄](https://www.aaai.org/Papers/AAAI/1999/AAAI99-049.pdf) | Sequentially learns a set of rules with SLIPPER |
| Bayesian rule set | [🗂️](https://csinva.io/imodels/rule_set/brs.html#imodels.rule_set.brs.BayesianRuleSetClassifier), [🔗](https://github.com/wangtongada/BOA), [📄](https://www.jmlr.org/papers/volume18/16-003/16-003.pdf) | Finds concise rule set with Bayesian sampling (slow) |
| Optimal rule list | [🗂️](https://csinva.io/imodels/rule_list/corels_wrapper.html#imodels.rule_list.corels_wrapper.OptimalRuleListClassifier), [🔗](https://github.com/corels/pycorels), [📄](https://www.jmlr.org/papers/volume18/17-716/17-716.pdf) | Fits rule list using global optimization for sparsity (CORELS) |
| Bayesian rule list | [🗂️](https://csinva.io/imodels/rule_list/bayesian_rule_list/bayesian_rule_list.html#imodels.rule_list.bayesian_rule_list.bayesian_rule_list.BayesianRuleListClassifier), [🔗](https://github.com/tmadl/sklearn-expertsys), [📄](https://arxiv.org/abs/1602.08610) | Fits compact rule list distribution with Bayesian sampling (slow) |
| Boosted rule set | [🗂️](https://csinva.io/imodels/rule_set/boosted_rules.html), [📄](https://www.sciencedirect.com/science/article/pii/S002200009791504X), [🔗](https://github.com/jaimeps/adaboost-implementation) | Sequentially fits a set of rules with Adaboost |
| Slipper rule set | [🗂️](https://csinva.io/imodels/rule_set/slipper.html), [📄](https://www.aaai.org/Papers/AAAI/1999/AAAI99-049.pdf) | Sequentially learns a set of rules with SLIPPER |
| Bayesian rule set | [🗂️](https://csinva.io/imodels/rule_set/brs.html#imodels.rule_set.brs.BayesianRuleSetClassifier), [📄](https://www.jmlr.org/papers/volume18/16-003/16-003.pdf), [🔗](https://github.com/wangtongada/BOA) | Finds concise rule set with Bayesian sampling (slow) |
| Optimal rule list | [🗂️](https://csinva.io/imodels/rule_list/corels_wrapper.html#imodels.rule_list.corels_wrapper.OptimalRuleListClassifier), [📄](https://www.jmlr.org/papers/volume18/17-716/17-716.pdf), [🔗](https://github.com/corels/pycorels) | Fits rule list using global optimization for sparsity (CORELS) |
| Bayesian rule list | [🗂️](https://csinva.io/imodels/rule_list/bayesian_rule_list/bayesian_rule_list.html#imodels.rule_list.bayesian_rule_list.bayesian_rule_list.BayesianRuleListClassifier), [📄](https://arxiv.org/abs/1602.08610), [🔗](https://github.com/tmadl/sklearn-expertsys) | Fits compact rule list distribution with Bayesian sampling (slow) |
| Greedy rule list | [🗂️](https://csinva.io/imodels/rule_list/greedy_rule_list.html), [🔗](https://medium.com/@penggongting/implementing-decision-tree-from-scratch-in-python-c732e7c69aea) | Uses CART to fit a list (only a single path), rather than a tree |
| OneR rule list | [🗂️](https://csinva.io/imodels/rule_list/one_r.html), &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;, [📄](https://link.springer.com/article/10.1023/A:1022631118932) | Fits rule list restricted to only one feature |
| Optimal rule tree | [🗂️](https://csinva.io/imodels/tree/gosdt/pygosdt.html#imodels.tree.gosdt.pygosdt.OptimalTreeClassifier), [🔗](https://github.com/Jimmy-Lin/GeneralizedOptimalSparseDecisionTrees), [📄](https://arxiv.org/abs/2006.08690) | Fits succinct tree using global optimization for sparsity (GOSDT) |
| Greedy rule tree | [🗂️](https://csinva.io/imodels/tree/cart_wrapper.html), [🔗](https://scikit-learn.org/stable/modules/tree.html), [📄](https://www.taylorfrancis.com/books/mono/10.1201/9781315139470/classification-regression-trees-leo-breiman-jerome-friedman-richard-olshen-charles-stone) | Greedily fits tree using CART |
| C4.5 rule tree | [🗂️](https://csinva.io/imodels/tree/c45_tree/c45_tree.html#imodels.tree.c45_tree.c45_tree.C45TreeClassifier), [🔗](https://github.com/RaczeQ/scikit-learn-C4.5-tree-classifier), [📄](https://link.springer.com/article/10.1007/BF00993309) | Greedily fits tree using C4.5 |
| TAO rule tree | [🗂️](https://csinva.io/imodels/tree/tao.html), &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;, ㅤㅤ[📄](https://proceedings.neurips.cc/paper/2018/hash/185c29dc24325934ee377cfda20e414c-Abstract.html) | Fits tree using alternating optimization |
| Iterative random<br/>forest | [🗂️](https://csinva.io/imodels/tree/iterative_random_forest/iterative_random_forest.html), [🔗](https://github.com/Yu-Group/iterative-Random-Forest), [📄](https://www.pnas.org/content/115/8/1943) | Repeatedly fit random forest, giving features with<br/>high importance a higher chance of being selected |
| Sparse integer<br/>linear model | [🗂️](https://csinva.io/imodels/algebraic/slim.html), &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;, ㅤㅤ[📄](https://link.springer.com/article/10.1007/s10994-015-5528-6) | Sparse linear model with integer coefficients |
| Tree GAM | [🗂️](https://csinva.io/imodels/algebraic/tree_gam.html), [🔗](https://github.com/interpretml/interpret), [📄](https://dl.acm.org/doi/abs/10.1145/2339530.2339556) | Generalized additive model fit with short boosted trees |
| <b>Greedy tree</br>sums (FIGS)</b> | [🗂️](https://csinva.io/imodels/figs.html), &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;, ㅤㅤ[📄](https://arxiv.org/abs/2201.11931) | Sum of small trees with very few total rules (FIGS) |
| <b>Hierarchical<br/> shrinkage wrapper</b> | [🗂️](https://csinva.io/imodels/shrinkage.html), &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;, ㅤㅤ[📄](https://arxiv.org/abs/2202.00858) | Improve a decision tree, random forest, or<br/>gradient-boosting ensemble with ultra-fast, post-hoc regularization |
| <b>RF+ (MDI+)</b> | [🗂️](https://csinva.io/imodels/mdi_plus.html), &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;, ㅤㅤ[📄](https://arxiv.org/pdf/2307.01932) | Flexible random forest-based feature importance |
| OneR rule list | [🗂️](https://csinva.io/imodels/rule_list/one_r.html), [📄](https://link.springer.com/article/10.1023/A:1022631118932) | Fits rule list restricted to only one feature |
| Optimal rule tree | [🗂️](https://csinva.io/imodels/tree/gosdt/pygosdt.html#imodels.tree.gosdt.pygosdt.OptimalTreeClassifier), [📄](https://arxiv.org/abs/2006.08690), [🔗](https://github.com/Jimmy-Lin/GeneralizedOptimalSparseDecisionTrees) | Fits succinct tree using global optimization for sparsity (GOSDT) |
| Greedy rule tree | [🗂️](https://csinva.io/imodels/tree/cart_wrapper.html), [📄](https://www.taylorfrancis.com/books/mono/10.1201/9781315139470/classification-regression-trees-leo-breiman-jerome-friedman-richard-olshen-charles-stone), [🔗](https://scikit-learn.org/stable/modules/tree.html) | Greedily fits tree using CART |
| C4.5 rule tree | [🗂️](https://csinva.io/imodels/tree/c45_tree/c45_tree.html#imodels.tree.c45_tree.c45_tree.C45TreeClassifier), [📄](https://link.springer.com/article/10.1007/BF00993309), [🔗](https://github.com/RaczeQ/scikit-learn-C4.5-tree-classifier) | Greedily fits tree using C4.5 |
| TAO rule tree | [🗂️](https://csinva.io/imodels/tree/tao.html), [📄](https://proceedings.neurips.cc/paper/2018/hash/185c29dc24325934ee377cfda20e414c-Abstract.html) | Fits tree using alternating optimization |
| Iterative random<br/>forest | [🗂️](https://csinva.io/imodels/tree/iterative_random_forest/iterative_random_forest.html), [📄](https://www.pnas.org/content/115/8/1943), [🔗](https://github.com/Yu-Group/iterative-Random-Forest) | Repeatedly fit random forest, giving features with<br/>high importance a higher chance of being selected |
| Sparse integer<br/>linear model | [🗂️](https://csinva.io/imodels/algebraic/slim.html), [📄](https://link.springer.com/article/10.1007/s10994-015-5528-6) | Sparse linear model with integer coefficients |
| Tree GAM | [🗂️](https://csinva.io/imodels/algebraic/tree_gam.html), [📄](https://dl.acm.org/doi/abs/10.1145/2339530.2339556), [🔗](https://github.com/interpretml/interpret) | Generalized additive model fit with short boosted trees |
| <b>Greedy tree</br>sums (FIGS)</b> | [🗂️](https://csinva.io/imodels/figs.html),ㅤ[📄](https://arxiv.org/abs/2201.11931) | Sum of small trees with very few total rules (FIGS) |
| <b>Hierarchical<br/> shrinkage wrapper</b> | [🗂️](https://csinva.io/imodels/shrinkage.html), [📄](https://arxiv.org/abs/2202.00858) | Improve a decision tree, random forest, or<br/>gradient-boosting ensemble with ultra-fast, post-hoc regularization |
| <b>RF+ (MDI+)</b> | [🗂️](https://csinva.io/imodels/mdi_plus.html), [📄](https://arxiv.org/pdf/2307.01932) | Flexible random forest-based feature importance |
| Distillation<br/>wrapper | [🗂️](https://csinva.io/imodels/util/distillation.html) | Train a black-box model,<br/>then distill it into an interpretable model |
| AutoML wrapper | [🗂️](https://csinva.io/imodels/util/automl.html) | Automatically fit and select an interpretable model |
| More models | ⌛ | (Coming soon!) Lightweight Rule Induction, MLRules, ... |

<p align="center">
<a href="https://csinva.io/imodels/">🗂️</a> Docs &emsp; 🔗 Reference code implementation &emsp; 📄 Research paper
</br>
</p>

## Demo notebooks

Demos are contained in the [notebooks](notebooks) folder.
Expand Down Expand Up @@ -316,17 +316,17 @@ Please cite the package if you use it in an academic work :)

```r
@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},
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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