RuleFit

Explainability Transparency

Description

RuleFit is a method that creates an interpretable model by combining linear terms with decision rules. It first extracts potential rules from ensemble trees, then builds a sparse linear model where those rules (binary conditions) and original features are used as predictors, with regularization to keep the model simple. The final model is a linear combination of a small set of rules and original features, balancing interpretability with predictive power.

Example Use Cases

Explainability

Building customer churn prediction models with rules like 'IF contract_length < 12_months AND support_calls > 5 THEN churn_risk = high', allowing marketing teams to understand and act on the key drivers of customer attrition.

Creating credit scoring models that combine traditional linear factors (income, age) with interpretable rules (IF recent_missed_payments = 0 AND account_age > 2_years THEN creditworthy), providing transparent lending decisions.

Transparency

Developing regulatory-compliant medical diagnosis models where treatment recommendations combine clinical measurements with clear decision rules (IF blood_pressure > 140 AND diabetes = true THEN high_risk), enabling audit trails for healthcare decisions.

Limitations

Can generate large numbers of rules even with regularisation, potentially overwhelming users and reducing practical interpretability.
Performance may be inferior to complex ensemble methods when rule complexity is constrained for interpretability.
Rule extraction quality depends heavily on the underlying tree ensemble, which may miss important feature interactions if not properly configured.
Requires careful hyperparameter tuning to balance between model complexity and interpretability, with no universal optimal setting.

Resources

christophM/rulefit

Software Package

Tree Ensembles with Rule Structured Horseshoe Regularization

Research Paper•Malte Nalenz and Mattias Villani•Feb 16, 2017

Safe RuleFit: Learning Optimal Sparse Rule Model by Meta Safe Screening

Research Paper•Hiroki Kato, Hiroyuki Hanada, and Ichiro Takeuchi•Oct 3, 2018

csinva/imodels

Software Package

Getting More From Regression Models with RuleFit | Towards Data ...

Tutorial

Related Techniques

Name	Description	Assurance Goals
Cross-validation	Cross-validation evaluates model performance and robustness by systematically partitioning data into multiple subsets (folds) and training/testing repeatedly on different combinations. Common approaches include k-fold (splitting into k equal parts), stratified (preserving class distributions), and leave-one-out variants. By testing on multiple independent holdout sets, it reveals how performance varies across different data subsamples, provides robust estimates of generalisation ability, and helps detect overfitting or model instability that single train-test splits might miss.	Reliability Transparency Fairness
Influence Functions	Influence functions quantify how much each training example influenced a model's predictions by computing the change in prediction that would occur if that training example were removed and the model retrained. Using calculus and the implicit function theorem, they approximate this 'leave-one-out' effect without actually retraining the model by computing gradients and Hessian information. This mathematical approach reveals which specific training examples were most responsible for pushing the model toward or away from particular predictions, enabling practitioners to trace problematic outputs back to their root causes in the training data.	Explainability Fairness Privacy
Permutation Tests	Permutation tests assess the statistical significance of observed results (such as model accuracy, feature importance, or group differences) by comparing them to what would occur purely by chance. The technique randomly shuffles labels or data thousands of times, recalculating the metric of interest each time to build an empirical null distribution. If the actual observed result falls in the extreme tail of this distribution (typically beyond the 95th or 99th percentile), it provides strong evidence that the relationship is genuine rather than due to random chance, without requiring parametric assumptions about data distributions.	Explainability Reliability
Prototype and Criticism Models	Prototype and Criticism Models provide data understanding by identifying two complementary sets of examples: prototypes represent the most typical instances that best summarise common patterns in the data, whilst criticisms are outliers or edge cases that are poorly represented by the prototypes. For example, in a dataset of customer transactions, prototypes might be the most representative buying patterns (frequent small purchases, occasional large purchases), whilst criticisms could be unusual behaviors (bulk buyers, one-time high-value customers). This dual approach reveals both what is normal and what is exceptional, helping understand data coverage and model blind spots.	Explainability Fairness
Coefficient Magnitudes (in Linear Models)	Coefficient Magnitudes assess feature influence in linear models by examining the absolute values of their coefficients. Features with larger absolute coefficients are considered to have a stronger impact on the prediction, while the sign of the coefficient indicates the direction of that influence (positive or negative). This technique provides a straightforward and transparent way to understand the direct linear relationship between each input feature and the model's output.	Explainability Transparency
Ridge Regression Surrogates	This technique approximates a complex model by training a ridge regression (a linear model with L2 regularization) on the original model's predictions. The ridge regression serves as a global surrogate that balances fidelity and interpretability, capturing the main linear relationships that the complex model learned while ignoring noise due to regularization.	Explainability Transparency

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