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

📚 docs • 📖 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.

```python from sklearn.modelselection import traintestsplit from imodels import getclean_dataset, HSTreeClassifierCV # import any imodels model here

prepare data (a sample clinical dataset)

X, y, featurenames = getcleandataset('csipecarnpred') Xtrain, Xtest, ytrain, ytest = traintestsplit( X, y, randomstate=42)

fit the model

model = HSTreeClassifierCV(maxleafnodes=4) # initialize a tree model and specify only 4 leaf nodes model.fit(Xtrain, ytrain, featurenames=featurenames) # fit model preds = model.predict(Xtest) # discrete predictions: shape is (ntest, 1) predsproba = model.predictproba(Xtest) # predicted probabilities: shape is (ntest, 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] ```

Installation

Install with pip install imodels (see here for help).

Supported models

🗂️ Docs   📄 Research paper   🔗 Reference code implementation

| Model | Reference | Description | | :-------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | | Rulefit rule set | 🗂️, 📄, 🔗 | Fits a sparse linear model on rules extracted from decision trees | | Skope rule set | 🗂️, 🔗 | Extracts rules from gradient-boosted trees, deduplicates them,
then linearly combines them based on their OOB precision | | Boosted rule set | 🗂️, 📄, 🔗 | Sequentially fits a set of rules with Adaboost | | Slipper rule set | 🗂️, 📄 | Sequentially learns a set of rules with SLIPPER | | Bayesian rule set | 🗂️, 📄, 🔗 | Finds concise rule set with Bayesian sampling (slow) | | Bayesian rule list | 🗂️, 📄, 🔗 | Fits compact rule list distribution with Bayesian sampling (slow) | | Greedy rule list | 🗂️, 🔗 | Uses CART to fit a list (only a single path), rather than a tree | | OneR rule list | 🗂️, 📄 | Fits rule list restricted to only one feature | | Optimal rule tree | 🗂️, 📄, 🔗 | Fits succinct tree using global optimization for sparsity (GOSDT) | | Greedy rule tree | 🗂️, 📄, 🔗 | Greedily fits tree using CART | | C4.5 rule tree | 🗂️, 📄, 🔗 | Greedily fits tree using C4.5 | | TAO rule tree | 🗂️, 📄 | Fits tree using alternating optimization | | Iterative random
forest | 🗂️, 📄, 🔗 | Repeatedly fit random forest, giving features with
high importance a higher chance of being selected | | Sparse integer
linear model | 🗂️, 📄 | Sparse linear model with integer coefficients | | Tree GAM | 🗂️, 📄, 🔗 | Generalized additive model fit with short boosted trees | | Greedy tree
sums (FIGS) | 🗂️,ㅤ📄 | Sum of small trees with very few total rules (FIGS) | | Hierarchical
shrinkage wrapper | 🗂️, 📄 | Improve a decision tree, random forest, or
gradient-boosting ensemble with ultra-fast, post-hoc regularization | | RF+ (MDI+) | 🗂️, 📄 | Flexible random forest-based feature importance | | Distillation
wrapper | 🗂️ | Train a black-box model,
then distill it into an interpretable model | | AutoML wrapper | 🗂️ | Automatically fit and select an interpretable model | | More models | ⌛ | (Coming soon!) Lightweight Rule Induction, MLRules, ... |

Demo notebooks

Demos are contained in the notebooks folder.

Quickstart colab demo

Shows how to fit, predict, and visualize with different interpretable models

What's the difference between the models?

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| | :----------------------------------------------------------: | :--------------------------------------------------------: | :-------------------------------------------------------: | | | | |

Support for different tasks

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 | BoostedRulesRegressor | | | SLIPPER rule set | SlipperClassifier | | | | Bayesian rule set | BayesianRuleSetClassifier | | 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 | | Tree GAM | TreeGAMClassifier | TreeGAMRegressor | | | Greedy tree sums (FIGS) | FIGSClassifier | FIGSRegressor | | | Hierarchical shrinkage | HSTreeClassifierCV | HSTreeRegressorCV | Wraps any sklearn tree-based model | | Distillation | | DistilledRegressor | Wraps any sklearn-compatible models | | AutoML model | AutoInterpretableClassifier️ | AutoInterpretableRegressor️ | |

Extras

Our favorite models

After developing and playing with imodels, we developed a few new models to overcome limitations of existing interpretable models.

FIGS: Fast interpretable greedy-tree sums

📄 Paper, 🔗 Post, 📌 Citation

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.

Hierarchical shrinkage: post-hoc regularization for tree-based methods

📄 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.

MDI+: Flexible Tree-Based Feature Importance

📄 Paper, 🔗 Post, 📌 Citation

MDI+ is a novel feature importance framework, which generalizes the popular mean decrease in impurity (MDI) importance score for random forests. At its core, MDI+ expands upon a recently discovered connection between linear regression and decision trees. In doing so, MDI+ enables practitioners to (1) tailor the feature importance computation to the data/problem structure and (2) incorporate additional features or knowledge to mitigate known biases of decision trees. In both real data case studies and extensive real-data-inspired simulations, MDI+ outperforms commonly used feature importance measures (e.g., MDI, permutation-based scores, and TreeSHAP) by substantional margins.

References

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}, }

```