对于Uplift Tree,类似 分类树可以集成随机森林、GBDT, Uplift Tree 也可以集成 CF(Casual Forest), BART(Bayesian additive regression trees),在很多文献中经常看到此类baseline。 分类模型(The class transformation)思路是通过某种变换,把问题转化为常规的Machine Learning问题解决。在我了解的范围里,业界应用案例不多。
1.1 决策树的概念 A Decision Tree is a Supervised Machine Learning algorithm which looks like an inverted tree, wherein each node represents a predictor variable (feature), the link between the nodes represents a Decision and each leaf node represents an outcome (response variable). 上面这个概念主...
machine learningexplainable artificial intelligenceFloods are one of the most common natural disasters in the world that affect all aspects of life, including human beings, agriculture, industry, and education. Research for developing models of flood predictions has been ongoing for the past few years...
Tree-based machine learning and deep learning approaches are widely applied in ozone (O3) retrieval, but they cannot achieve high accuracy and interpretability simultaneously. To overcome this limitation, a tree-based ensemble deep learning model, named semi-SILDM, was proposed for O3 prediction at...
Machine learning holds the promise of learning the energy functional via examples, bypassing the need to solve complicated quantum-chemical equations and realizing efficient computing of molecular electronic properties. However, a single... Wang,Ruobing - 《Journal of Physical Chemistry C》 被引量: ...
In this paper, two novel hybrid decision tree-based machine learning models are proposed to obtain more accurate short-term water quality prediction results. The basic models of the two hybrid models are extreme gradient boosting (XGBoost) and random forest (RF), which respectively introduce an ...
Tree-based这类方法,和之前meta-learning 类的方法最明显的区别是:这类方法把causal effect 的计算显示的加入了到了树模型节点分裂的标准中从 response时代过渡到了effect时代。 大量的这类算法基本围绕着树节点分裂方式做文章,普遍采用的是兼容性比较高的[[万字长文讲述树模型的历史|cart树]] ...
Gully erosion is a worldwide threat with numerous environmental, social, and economic impacts. The purpose of this research is to evaluate the performance and robustness of six machine learning ensemble models based on the decision tree principle: Random
This paper presents a regression tree ensemble‐based machine learning model to predict the fatigue life for fiber metal laminates. The intercorrelations between the multiple input parameters (material properties) and the fatigue life span is investigated. Good model accuracy and model reliability are ...
Exploring Tree-Based Machine Learning Models to Estimate the Ultimate Pile Capacity From Cone Penetration Test Data Several approaches have been developed to estimate the ultimate capacity of piles, such as static and dynamic load tests, static analysis from soil borings... MM Shoaib,MY Abu-Farsakh...