Supersparse Linear Integer Models for Predictive Scoring Systems

Supersparse Linear Integer Models for Interpretable Classification

Scoring systems are classification models that only require users to add, subtract and multiply a few meaningful numbers to make a prediction. These models are often used because they are practical and interpretable. In this paper, we introduce an off-the-shelf tool to create scoring systems that both accurate and interpretable, known as a Supersparse Linear Integer Model (SLIM). SLIM is a discrete optimization problem that minimizes the 0-1 loss to encourage a high level of accuracy, regularizes the L0-norm to encourage a high level of sparsity, and constrains coefficients to a set of interpr…

Supersparse Linear Integer Models for Optimized Medical Scoring Systems

Scoring systems are linear classification models that only require users to add, subtract and multiply a few small numbers in order to make a prediction. These models are in widespread use by the medical community, but are difficult to learn from data because they need to be accurate and sparse, have coprime integer coefficients, and satisfy multiple operational constraints. We present a new method for creating data-driven scoring systems called a Supersparse Linear Integer Model (SLIM). SLIM scoring systems are built by solving an integer program that directly encodes measures of accuracy (th…

基于评分规则构建用于概率预测的树

Building Trees for Probabilistic Prediction via Scoring Rules

针对标准决策树生成的预测分布效果不佳的问题，提出用基于严格评分规则的分裂准则改进决策树，经模拟和真实数据集验证，新方法提升了树的整体预测分布性能。

Predictive Value Generalization Bounds

In this paper, we study a bi-criterion framework for assessing scoring functions in the context of binary classification. The positive and negative predictive values (ppv and npv, respectively) are conditional probabilities of the true label matching a classifier's predicted label. The usual classification error rate is a linear combination of these probabilities, and therefore, concentration inequalities for the error rate do not yield confidence intervals for the two separate predictive values. We study generalization properties of scoring functions with respect to predictive values by deriv…

对信用评分问题中公平性意识机器学习的实验研究

An Experimental Study on Fairness-aware Machine Learning for Credit Scoring Problems

本文研究信用评分中公平性意识机器学习的关键方面，评估公平性模型与传统分类模型的平衡性，发现公平性模型在预测准确性和公平性间取得更好平衡。

从分类准确率到恰当评分规则：概率 Top 列表预测的可引出性

From Classification Accuracy to Proper Scoring Rules: Elicitability of Probabilistic Top List Predictions

本文提出概率top列表作为分类预测新形式，并基于对称恰当评分规则构建可引出该泛函的严格一致评估度量，其中Brier分数尤为适用，从而统一比较单类预测与完整预测分布。