Averaging predictions from multiple competing inferential models frequently
outperforms predictions from any single model, providing that models are
optimally weighted to maximize predictive performance. This is particularly the
case in so-called $\mathcal{M}$-open settings where the true model is not in
the set of candidate models, and may be neither mathematically reifiable nor
known precisely. This practice of model averaging has a rich history in
statistics and machine learning, and there are currently a number of methods to
estimate the weights for constructing model-averaged predictive distributions.
Nonetheless, there are few existing software packages that can estimate model
weights from the full variety of methods available, and none that blend model
predictions into a coherent predictive distribution according to the estimated
weights. In this paper, we introduce the BayesBlend Python package, which
provides a user-friendly programming interface to estimate weights and blend
multiple (Bayesian) models' predictive distributions. BayesBlend implements
pseudo-Bayesian model averaging, stacking and, uniquely, hierarchical Bayesian
stacking to estimate model weights. We demonstrate the usage of BayesBlend with
examples of insurance loss modeling.

该研究论文介绍了 BayesBlend Python 软件包，它提供了一个用户友好的编程接口，用于估计权重并混合多个（贝叶斯）模型的预测分布。通过保龄球模型均衡、堆叠和独特的层次贝叶斯堆叠，BayesBlend 演示了保险损失建模的实例。

BayesBlend: 使用伪贝叶斯模型平均法、叠加法和分层叠加法简化模型融合的 Python 实现

BayesBlend: Easy Model Blending using Pseudo-Bayesian Model Averaging,  Stacking and Hierarchical Stacking in Python

Ensembles are important tools for improving the performance of machine
learning models. In cases related to natural language processing, ensembles
boost the performance of a method due to multiple large models available in
open source. However, existing approaches mostly rely on simple averaging of
predictions by ensembles with equal weights for each model, ignoring
differences in the quality and conformity of models. We propose to estimate
weights for ensembles of NLP models using not only knowledge of their
individual performance but also their similarity to each other. By adopting
distance measures based on Topological Data Analysis (TDA), we improve our
ensemble. The quality improves for both text classification accuracy and
relevant uncertainty estimation.

使用拓扑数据分析 (TDA) 基于距离测量，提出了估计自然语言处理 (NLP) 模型权重的方法，通过对模型的性能和相似性进行综合评估，改进了集成学习方法，提高了文本分类准确性和相关的不确定性估计。

基于拓扑数据分析的语言模型多样性集成

Diversity-Aware Ensembling of Language Models Based on Topological Data  Analysis

This paper describes experiments, on two domains, to investigate the effect
of averaging over predictions of multiple decision trees, instead of using a
single tree. Other authors have pointed out theoretical and commonsense reasons
for preferring the multiple tree approach. Ideally, we would like to consider
predictions from all trees, weighted by their probability. However, there is a
vast number of different trees, and it is difficult to estimate the probability
of each tree. We sidestep the estimation problem by using a modified version of
the ID3 algorithm to build good trees, and average over only these trees. Our
results are encouraging. For each domain, we managed to produce a small number
of good trees. We find that it is best to average across sets of trees with
different structure; this usually gives better performance than any of the
constituent trees, including the ID3 tree.

本文研究使用多棵决策树对预测结果进行平均，比使用单棵决策树更能提高预测准确性。作者利用修改版的 ID3 算法选出优秀的决策树进行平均，证明了结构不同的决策树集合进行平均比单棵决策树更优，提高了预测性能。