Are you sure you want to delete this access key?
Python package for concise, transparent, and accurate predictive modeling. All sklearn-compatible and easily customizable.
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 BayesianRuleListClassifier, GreedyRuleListClassifier, SkopeRulesClassifier # see more models below
from imodels import SLIMRegressor, RuleFitRegressor
model = BayesianRuleListClassifier() # 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).
Model | Reference | Description |
---|---|---|
Rulefit rule set | 🗂️, 🔗, 📄 | Extracts rules from a decision tree then builds a sparse linear model with them |
Skope rule set | 🗂️, 🔗 | Extracts rules from gradient-boosted trees, deduplicates them, then forms a linear combination of them based on their OOB precision |
Boosted rule set | 🗂️, 🔗, 📄 | Uses Adaboost or SLIPPER to sequentially learn a set of rules |
BOA rule set | 🗂️, 🔗, 📄 | Uses a Bayesian or-of-and algorithm to find a concise rule set. |
Bayesian rule list | 🗂️, 🔗, 📄 | Learns a compact rule list by sampling rule lists (rather than using a greedy heuristic) |
Greedy rule list | 🗂️, 🔗 | Uses CART to learn a list (only a single path), rather than a decision tree |
OneR rule list | 🗂️, 📄 | Learns rule list restricted to only one feature |
Optimal rule tree | 🗂️, 🔗, 📄 | (In progress) Learns succinct trees using global optimization rather than greedy heuristics |
Iterative random forest | 🗂️, 🔗, 📄 | (In progress) Repeatedly fit random forest, giving features with high importance a higher chance of being selected. |
Sparse integer linear model | 🗂️, 📄 | Forces coefficients to be integers |
Rule sets | ⌛ | (Coming soon!) Many popular rule sets including Lightweight Rule Induction, MLRules |
Docs 🗂️, Reference code implementation 🔗, Research paper 📄
See also our fast and effective discretizers for data preprocessing.
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 pruning / combination |
---|---|---|
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:
Model | Binary classification | Regression |
---|---|---|
Rulefit rule set | ✔️ | ✔️ |
Skope rule set | ✔️ | |
Boosted rule set | ✔️ | |
Bayesian rule list | ✔️ | |
Greedy rule list | ✔️ | |
OneR rule list | ✔️ | |
Optimal rule tree | ||
Iterative random forest | ||
Sparse integer linear model | ✔️ | ✔️ |
If it's useful for you, please cite the package using the below, 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},
}
Press p or to see the previous file or, n or to see the next file
Are you sure you want to delete this access key?
Are you sure you want to delete this access key?
Are you sure you want to delete this access key?
Are you sure you want to delete this access key?