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Found 10 papers, 0 papers with code
Monotone Tree-Based GAMI Models by Adapting XGBoost
It is straightforward to fit a monotone model to $f(x)$ using the options in XGBoost.
Computing SHAP Efficiently Using Model Structure Information
Finally, if even the order of model is unknown, we propose an iterative way to approximate Shapley values.
Interpretable Machine Learning based on Functional ANOVA Framework: Algorithms and Comparisons
To understand and explain the model results, one had to rely on post hoc explainability techniques, which are known to have limitations.
Using Model-Based Trees with Boosting to Fit Low-Order Functional ANOVA Models
We propose a new algorithm, called GAMI-Tree, that is similar to EBM, but has a number of features that lead to better performance.
Shapley Computations Using Surrogate Model-Based Trees
Shapley-related techniques have gained attention as both global and local interpretation tools because of their desirable properties.
Performance and Interpretability Comparisons of Supervised Machine Learning Algorithms: An Empirical Study
Overall, XGB and FFNNs were competitive, with FFNNs showing better performance in smooth models and tree-based boosting algorithms performing better in non-smooth models.
Explaining Adverse Actions in Credit Decisions Using Shapley Decomposition
The applicant is then entitled to an explanation for the negative decision.
Supervised Machine Learning Techniques: An Overview with Applications to Banking
This article provides an overview of Supervised Machine Learning (SML) with a focus on applications to banking.
Surrogate Locally-Interpretable Models with Supervised Machine Learning Algorithms
Supervised Machine Learning (SML) algorithms, such as Gradient Boosting, Random Forest, and Neural Networks, have become popular in recent years due to their superior predictive performance over traditional statistical methods.
Locally Interpretable Models and Effects based on Supervised Partitioning (LIME-SUP)
This is in contrast with the KLIME approach that is based on clustering the predictor space.
