Dimensionality Reduction (DR) techniques such as t-SNE and UMAP are popular
for transforming complex datasets into simpler visual representations. However,
while effective in uncovering general dataset patterns, these methods may
introduce artifacts and suffer from interpretability issues. This paper
presents DimVis, a visualization tool that employs supervised Explainable
Boosting Machine (EBM) models (trained on user-selected data of interest) as an
interpretation assistant for DR projections. Our tool facilitates
high-dimensional data analysis by providing an interpretation of feature
relevance in visual clusters through interactive exploration of UMAP
projections. Specifically, DimVis uses a contrastive EBM model that is trained
in real time to differentiate between the data inside and outside a cluster of
interest. Taking advantage of the inherent explainable nature of the EBM, we
then use this model to interpret the cluster itself via single and pairwise
feature comparisons in a ranking based on the EBM model's feature importance.
The applicability and effectiveness of DimVis are demonstrated through two use
cases involving real-world datasets, and we also discuss the limitations and
potential directions for future research.

DimVis 是一个利用监督可解释的增强机器模型的可视化工具，通过交互式探索 UMAP 投影来提供高维数据分析的特征相关性解释，在转化复杂数据集为简单视觉表示时，增加了解释性，并解决了引入人为失真和解释性问题的困扰。

DimVis：可解释的增强机构在降维中解释视觉聚类

DimVis: Interpreting Visual Clusters in Dimensionality Reduction With  Explainable Boosting Machine

Automatic Text Summarization (ATS) is becoming relevant with the growth of
textual data; however, with the popularization of public large-scale datasets,
some recent machine learning approaches have focused on dense models and
architectures that, despite producing notable results, usually turn out in
models difficult to interpret. Given the challenge behind interpretable
learning-based text summarization and the importance it may have for evolving
the current state of the ATS field, this work studies the application of two
modern Generalized Additive Models with interactions, namely Explainable
Boosting Machine and GAMI-Net, to the extractive summarization problem based on
linguistic features and binary classification.

本文研究了可解释的机器学习模型在自动文本摘要中的应用，探讨了基于语言学特征和二分类的解释性 Boosting 机器和 GAMI-Net 模型在摘要提取问题中的应用。

利用广义可加模型及交互作用进行抽取式文本摘要

Extractive Text Summarization Using Generalized Additive Models with Interactions for Sentence Selection

InterpretML is an open-source Python package which exposes machine learning
interpretability algorithms to practitioners and researchers. InterpretML
exposes two types of interpretability - glassbox models, which are machine
learning models designed for interpretability (ex: linear models, rule lists,
generalized additive models), and blackbox explainability techniques for
explaining existing systems (ex: Partial Dependence, LIME). The package enables
practitioners to easily compare interpretability algorithms by exposing
multiple methods under a unified API, and by having a built-in, extensible
visualization platform. InterpretML also includes the first implementation of
the Explainable Boosting Machine, a powerful, interpretable, glassbox model
that can be as accurate as many blackbox models. The MIT licensed source code
can be downloaded from github.com/microsoft/interpret.

InterpetML 是一个开源的 Python 包，可以帮助实践者和研究人员暴露机器学习算法并提供解释能力。该软件包在统一 API 下公开多种方法，并具有内置的、可扩展的可视化平台，同时还包括可与许多黑盒模型一样准确的可解释的强大的 glassbox 模型 EBM 等工具。