readme.md

August 1, 2026 · View on GitHub

Python package for concise, transparent, and accurate predictive modeling.
All sklearn-compatible and easy to use.
Check out our new packages! Interpretability in text: imodelsX, interpretability tools for tabular data with agents: agentic-imodels

📚 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.

from imodels import get_clean_dataset, HSTreeClassifierCV # import any imodels model here
from sklearn.model_selection import train_test_split

# 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]

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
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
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 demo

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

Autogluon demo

Fit/select an interpretable model automatically using Autogluon AutoML

Quickstart colab demo

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

Clinical decision rule notebook

Shows an example of using imodels for deriving a clinical decision rule

Posthoc analysis

We also include some demos of posthoc analysis, which occurs after fitting models: posthoc.ipynb shows different simple analyses to interpret a trained model and uncertainty.ipynb contains basic code to get uncertainty estimates for a model

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

Ex. RuleFit vs. SkopeRules

RuleFit and SkopeRules differ only in the way they prune rules: RuleFit uses a linear model whereas SkopeRules heuristically deduplicates rules sharing overlap.

Ex. Bayesian rule lists vs. greedy rule lists

Bayesian rule lists and greedy rule lists differ in how they select rules; bayesian rule lists perform a global optimization over possible rule lists while Greedy rule lists pick splits sequentially to maximize a given criterion.

Ex. FPSkope vs. SkopeRules

FPSkope and SkopeRules differ only in the way they generate candidate rules: FPSkope uses FPgrowth whereas SkopeRules extracts rules from decision trees.

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):

All of these models follow the standard sklearn estimator API, which is checked for every model in tests/model_api_test.py: fit returns the estimator, predict returns labels drawn from classes_ (strings included), predict_proba returns an (n_samples, n_classes) matrix whose rows sum to 1, DataFrame input sets feature_names_in_,, models can be cloned and configured with get_params/set_params, and every model works inside sklearn pipelines and grid searches.

Model	Binary classification	Regression	Notes
Rulefit rule set	RuleFitClassifier	RuleFitRegressor
Skope rule set	SkopeRulesClassifier
FPSkope rule set	FPSkopeClassifier		Like Skope, but generates candidate rules with FPGrowth; requires discretized features
Rulefit rule set (XGBoost)	pass `tree_generator=XGBClassifier(...)` to `RuleFitClassifier`	pass `tree_generator=XGBRegressor(...)` to `RuleFitRegressor`	Requires xgboost
FPLasso rule set	FPLassoClassifier	FPLassoRegressor	Lasso over rules mined with FPGrowth; requires discretized features
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
Greedy rule tree (CART)	GreedyTreeClassifier	GreedyTreeRegressor
C4.5 rule tree	C45TreeClassifier
CCP-pruned rule tree	DecisionTreeCCPClassifier	DecisionTreeCCPRegressor	Prunes a tree to a target complexity via cost-complexity pruning
TAO rule tree	TaoTreeClassifier	TaoTreeRegressor
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
Marginal shrinkage linear model		MarginalShrinkageLinearModelRegressor	Linear model shrunk towards its marginal effects
BART		BART	Bayesian additive regression trees (slow)
Distillation		DistilledRegressor	Wraps any sklearn-compatible models
AutoML model	AutoInterpretableClassifier️	AutoInterpretableRegressor️

Inspecting the rules a model learned

Every rule-based model exposes its rules the same way, as a pandas DataFrame with one row per rule, via get_rules():

from imodels import FIGSClassifier

model = FIGSClassifier(max_rules=4).fit(X_train, y_train, feature_names=feature_names)
model.get_rules()

                                               rule  prediction  tree
0                        FocalNeuroFindings2 <= 0.5       0.117     0
1                         FocalNeuroFindings2 > 0.5       0.427     0
2                             HighriskDiving <= 0.5      -0.008     1
3                              HighriskDiving > 0.5       0.550     1
4  PainNeck2 <= 0.5 and AlteredMentalStatus2 <= 0.5      -0.083     2
5   PainNeck2 <= 0.5 and AlteredMentalStatus2 > 0.5       0.048     2
6                                   PainNeck2 > 0.5       0.058     2

Two columns are always present: rule, the condition as a string, and prediction, what that rule predicts. Models add their own columns on top — coef, support and importance for RuleFit, tree for models made of several trees, and weight for boosted ensembles, which combine their trees by weighted vote. Where a model is additive, as FIGS is, prediction is that tree's contribution, so the contributions of the matching rules sum to the model's output.

This works across rule sets, rule lists and tree-based models (RuleFit, SkopeRules, SLIPPER, greedy and Bayesian rule lists, FIGS, CART, C4.5, TAO, boosted rules, and hierarchical shrinkage, including the CV variants). It is also available as a function, imodels.get_rules(model), and takes an optional feature_names argument to rename the features. Models that aren't rule-based raise a clear error.

Tree-based models expose feature_importances_ (mean decrease in impurity), the same measure sklearn's tree models report, so they can be compared directly.

Tree-based models also expose apply(X), which reports which leaf each sample falls into, using the same node numbering as scikit-learn. A single tree returns one index per sample; a model made of several trees (FIGS, boosted rules) returns one column per tree, like RandomForest.apply.

Extras

Data-wrangling functions for working with popular tabular datasets (e.g. compas).

These functions, in conjunction with imodels-data and imodels-experiments, make it simple to download data and run experiments on new models.

Explain classification errors with a simple posthoc function.

Fit an interpretable model to explain a previous model's errors (ex. in this notebook📓).

Fast and effective discretizers for data preprocessing.

Discretizer	Reference	Description
MDLP	🗂️, 🔗, 📄	Discretize using entropy minimization heuristic
Simple	🗂️, 🔗	Simple KBins discretization
Random Forest	🗂️	Discretize into bins based on random forest split popularity

Rule-based utils for customizing models

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.

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

Readings

Interpretable ML good quick overview: murdoch et al. 2019, pdf
Interpretable ML book: molnar 2019, pdf
Case for interpretable models rather than post-hoc explanation: rudin 2019, pdf
Review on evaluating interpretability: doshi-velez & kim 2017, pdf

Reference implementations (also linked above)

The code here heavily derives from the wonderful work of previous projects. We seek to to extract out, unify, and maintain key parts of these projects.

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

Related packages

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
desdeo-brb: distributional rule-based models

Updates

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!

Please cite the package if you use it in an academic work :)

@software{
	singh2021imodels,
	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},
}