FasterRisk: Fast and Accurate Interpretable Risk Scores

@article{Liu2022FasterRiskFA,
  title={FasterRisk: Fast and Accurate Interpretable Risk Scores},
  author={Jiachang Liu and Chudi Zhong and Boxuan Li and Margo I. Seltzer and Cynthia Rudin},
  journal={ArXiv},
  year={2022},
  volume={abs/2210.05846}
}
Over the last century, risk scores have been the most popular form of predictive model used in healthcare and criminal justice. Risk scores are sparse linear models with integer coefficients; often these models can be memorized or placed on an index card. Typically, risk scores have been created either without data or by rounding logistic regression coefficients, but these methods do not reliably produce high-quality risk scores. Recent work used mathematical programming, which is… 

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References

SHOWING 1-10 OF 42 REFERENCES

Learning Optimized Risk Scores

A new machine learning approach to learn risk scores that can fit risk scores in a way that scales linearly in the number of samples, provides a certificate of optimality, and obeys real-world constraints without parameter tuning or post-processing is presented.

Optimized Risk Scores

This paper forms a principled approach to learn risk scores that are fully optimized for feature selection, integer coefficients, and operational constraints, and presents a new cutting plane algorithm to efficiently recover its optimal solution.

Interval Coded Scoring: a toolbox for interpretable scoring systems

The presented toolbox interface makes Interval Coded Scoring theory easily applicable to both small and large datasets, and allows end users to make a trade-off between complexity and performance based on cross-validation results and expert knowledge.

Supersparse Linear Integer Models for Predictive Scoring Systems

Supersparse Linear Integer Models (SLIM) produces scoring systems that are accurate and interpretable using a mixed-integer program (MIP) whose objective penalizes the training error, L0-norm and L1-norm of its coefficients.

Supersparse linear integer models for optimized medical scoring systems

This paper provides bounds on the testing and training accuracy of SLIM scoring systems, and presents a new data reduction technique that can improve scalability by eliminating a portion of the training data beforehand.

A Provable Algorithm for Learning Interpretable Scoring Systems

This work introduces an original methodology to simultaneously learn interpretable binning mapped to a class variable, and the weights associated with these bins contributing to the score, and develops and shows the theoretical guarantees for this method.

The fused lasso penalty for learning interpretable medical scoring systems

An original methodology to simultaneously learn interpretable binning mapped to a class variable, and the weights associated with these bins contributing to the score is introduced.

A Bayesian Approach to Learning Scoring Systems

A Bayesian method for building scoring systems, which are linear models with coefficients that have very few significant digits, is presented, which achieves a high degree of interpretability of the models while maintaining competitive generalization performances.

Fast Sparse Classification for Generalized Linear and Additive Models

This work proposes the exponential loss, which permits an analytical solution to the line search at each iteration of the logistic loss, and produces interpretable models that have accuracy comparable to black box models on challenging datasets.

In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction

This work revisits the recidivism prediction problem using state-of-the-art tools from interpretable machine learning, and assessing the models for performance, interpretability, and fairness, and presents multiple models that beat existing risk assessments in performance.