Gradient Boost Decision Trees (GBDT) is a powerful additive model based on
tree ensembles. Its nature makes GBDT a black-box model even though there are
multiple explainable artificial intelligence (XAI) models obtaining information
by reinterpreting the model globally and locally. Each tree of the ensemble is
a transparent model itself but the final outcome is the result of a sum of
these trees and it is not easy to clarify.
In this paper, a feature contribution method for GBDT is developed. The
proposed method takes advantage of the GBDT architecture to calculate the
contribution of each feature using the residue of each node. This algorithm
allows to calculate the sequence of node decisions given a prediction.
Theoretical proofs and multiple experiments have been carried out to
demonstrate the performance of our method which is not only a local
explicability model for the GBDT algorithm but also a unique option that
reflects GBDTs internal behavior. The proposal is aligned to the contribution
of characteristics having impact in some artificial intelligence problems such
as ethical analysis of Artificial Intelligence (AI) and comply with the new
European laws such as the General Data Protection Regulation (GDPR) about the
right to explain and nondiscrimination.

GBDT 模型是一种基于树集合的强大的加性模型，本文开发了一种特征贡献方法，通过利用 GBDT 架构来计算每个特征的贡献，从而提供了一种对 GBDT 算法进行局部可解释性的模型。该方法在理论证明和多个实验中得到了验证，并且既能反映 GBDT 的内部行为也符合欧洲 GDPR 等相关法律的要求。

在梯度提升树中实现本地可解释性：特征贡献

Implementing local-explainability in Gradient Boosting Trees: Feature  Contribution

Power demand forecasting is a crucial and challenging task for new power
system and integrated energy system. However, as public feature databases and
the theoretical mechanism of power demand changes are unavailable, the known
features of power demand fluctuation are much limited. Recently, multimodal
learning approaches have shown great vitality in machine learning and AIGC. In
this paper, we interact two modal data and propose a textual-knowledge-guided
numerical feature discovery (TKNFD) method for short-term power demand
forecasting. TKNFD extensively accumulates qualitative textual knowledge,
expands it into a candidate feature-type set, collects numerical data of these
features, and eventually builds four-dimensional multivariate source-tracking
databases (4DM-STDs). Next, TKNFD presents a two-level quantitative feature
identification strategy independent of forecasting models, finds 43-48
features, and systematically analyses feature contribution and dependency
correlation. Benchmark experiments in two different regions around the world
demonstrate that the forecasting accuracy of TKNFD-discovered features reliably
outperforms that of SoTA feature schemes by 16.84% to 36.36% MAPE. In
particular, TKNFD reveals many unknown features, especially several dominant
features in the unknown energy and astronomical dimensions, which extend the
knowledge on the origin of strong randomness and non-linearity in power demand
fluctuation. Besides, 4DM-STDs can serve as public baseline databases.

提出了一种基于文本知识引导的数字特征发现（TKNFD）方法，通过互动两种模态数据对短期功率需求进行预测，实验结果表明 TKNFD 发现的特征在准确性上优于当前主流特征方案达 16.84% 至 36.36% 的 MAPE，并揭示了功率需求波动中强随机性和非线性性的起源。

基于文本知识引导的电力需求预测的数值特征发现方法

Textual-Knowledge-Guided Numerical Feature Discovery Method for Power  Demand Forecasting

This paper studies the problem of estimating the contributions of features to
the prediction of a specific instance by a machine learning model and the
overall contribution of a feature to the model. The causal effect of a feature
(variable) on the predicted outcome reflects the contribution of the feature to
a prediction very well. A challenge is that most existing causal effects cannot
be estimated from data without a known causal graph. In this paper, we define
an explanatory causal effect based on a hypothetical ideal experiment. The
definition brings several benefits to model agnostic explanations. First,
explanations are transparent and have causal meanings. Second, the explanatory
causal effect estimation can be data driven. Third, the causal effects provide
both a local explanation for a specific prediction and a global explanation
showing the overall importance of a feature in a predictive model. We further
propose a method using individual and combined variables based on explanatory
causal effects for explanations. We show the definition and the method work
with experiments on some real-world data sets.

本研究旨在解决机器学习模型预测个体实例时特征贡献和整体特征重要性的估计问题，提出了一种基于假设理想实验的因果效应定义，并构建了基于因果效应的透明且有意义的本地和全局解释方法，其数据驱动估计和实验验证表明了该方法的有效性及实用性。