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三支决策理论

三支决策理论

三⽀决策理论三⽀决策(Three-way Decision)是⼀种基于符合⼈类认知的决策模式，它认为：⼈们在实际决策过程中，对于具有充分把握接受或拒绝的事物能够⽴即作出快速的判断；对于哪些不能⽴即作出决策的事物，⼈们往往会推迟对事件的判断，即：延迟决策。

造成延迟决策的原因很多，⽐如：所掌握的信息不够充分、对风险的评估不够全⾯、对事件的认知不够彻底等。

当⼈们对信息、风险、认知的掌握程度达到⼀定的⽔平，会作出接受或拒绝的最终判断，从这个⾓度说，三⽀决策是最终实现⼆⽀决策的⼀个中间步骤。

此外，三⽀决策有着⼗分⼴泛的应⽤背景。

例如：在论⽂的审稿过程中，对于⼀篇稿件，如果⼗分优秀则直接接收，如果质量太差则直接拒稿。

但是在⼤多情况下，稿件可能具有⼀定的创新性，但技术、语⾔等⽅⾯都需要进⼀步提⾼，主编往往选择修改和重审。

在医学治疗中，讲究听闻望切，对于⼀些⼩病⽽⾔，医⽣能够快速准确地作出有病或⽆病的诊断；⽽对于⼀些疑难杂症，需要通过进⾏⼀些检查才能进⼀步的确诊。

三⽀决策的思想已在医学、⼯程、管理、信息领域得到了成功的应⽤。

近⼏年来，对于三⽀决策和粒计算的研究引起了国内外学者的⼴泛关注，在2009-2012年连续四届国际粗糙集与知识技术学术会议(RSKT)以及2011-2012年连续两届中国粗糙集与软计算学术会议(CRSSC)上都举办了三⽀决策与粒计算的研讨会，李华雄等编著的《决策粗糙集理论及其研究进展》以及贾修⼀等编著的《三⽀决策理论与应⽤》推动了三⽀决策与粒计算的发展，国际著名SCI期刊《International Journal of Approximate Reasoning》和《Fundamenta Informaticae》等也先后出版专刊推动该主题的发展。

粒计算（Granular Computing）是当前计算智能研究领域中模拟⼈类思维和解决复杂问题的新⽅法。

它覆盖了所有有关粒度的理论、⽅法和技术，是研究复杂问题求解、海量数据挖掘和模糊信息处理等问题的有⼒⼯具。

第6讲 树类模型

第6讲 树类模型

树类模型问题引入某连锁餐饮企业手头拥有一批数据，想了解周末和非周末对销量是否有很大区别，以及天气的好坏、是否有促销活动对销量的影响。

单击此处编辑母版标题样式决策树学习算法1熵、信息增益、基尼指数2Bagging与随机森林3Adaboost/GDBT4各种树模型优缺点及应用场景5案例演示6目 录CONTENTS1chapter 决策树学习算法1chapter决策树学习算法概览决策树是一个两阶段过程，包括模型学习阶段（构建分类模型）和分类预测阶段（使用模型预测类标号）。

决策树分类算法属于监督学习（Supervised learning），即样本数据中有类别标号，构建使用递归的方法依次进行。

一阶段（以分类为例）：可以看做是根据样本来学习一个映射或函数y=f(x)表达式，能够使用它预测给定元组X的类标号y。

第二阶段：使用第一阶段学习得到的模型进行分类。

首先评估分类器的预测准确率。

这个过程要尽量减少过拟合。

单击此处编辑母版标题样式2chapter 熵、信息增益、基尼指数1chapter 2chapter熵（Entropy）在信息论中，熵是对不确定性（离散程度或混乱程度）的一种度量，用来对信息进行量化。

熵越大，不确定性越大，信息量也就越大。

根据熵的特性，可以通过计算熵值来判断一个事件的随机性及无序程度，也可以用熵值来判断某个指标的离散程度，指标的离散程度越大，该指标对综合评价的影响（权重）越大。

假如样本数据在某指标下取值都相等，则该指标对总体评价的影响为0，即权值为0.单击此处编辑母版标题样式熵权法是一种客观赋权法，因为它仅依赖于数据本身的离散性。

假设在一个集合D中第i类样本所占的比例为pi（i=1,2,3…n），则D的信息熵可表示为：单击此处编辑母版标题样式条件熵（在特定变量发生的条件下指定变量发生与否的熵）现在我们假设将训练数据D 按属性A 进行划分，假设属性A 有v 个可能的取值，则按A 属性进行分裂出的v 个子集（即树中的v 个分支），每个可能取值集合为Dj ，则A 属性的条件熵计算方法为（|Dj|和|D|表示集合中元素的个数）：信息增益（不确定性的减少程度）信息熵减去条件熵，表示此条件对于信息熵减少的程度，即可以对信息的判断减少多少不确定性，数值越大，表示某个条件熵对信息熵减少程序越大，也就是说，这个属性对于信息的判断起到的作用越大。

决策树-上-ID3 C4.5 CART 及剪枝

决策树-上-ID3 C4.5 CART 及剪枝

Entropy(S ) pi log 2 ( pi )
i 1
C
9 9 5 5 log log 0.940286 14 14 14 14
2 2 3 3 Entropy ( Soutlook sunny ) log log 0.970951 5 5 5 5 3 3 2 2 Entropy ( Soutlook rain ) log log 0.970951 5 5 5 5
i 1 C C vVofF

p (v)Entropy ( Sv ) p (v){ pvj log 2 ( pvj )}
j 1 C
pi log 2 ( pi )
i 1 C
vVofF

pi log 2 ( pi )
i 1
vVofF

p(v) pvj log 2 ( pvj )
– 创建根节点R – 如果当前DataSet中的数据都属于同一类，则标记R的类别为该 类 – 如果当前featureList 集合为空，则标记R的类别为当前 DataSet 中样本最多的类别 – 递归情况：
• 从featureList中选择属性F（选择Gain(DataSet, F)最大的属性） • 根据F的每一个值v，将DataSet划分为不同的子集DS，对于每一个DS：
– 基于错误剪枝EBP(Error-Based Pruning)
C4.5-连续型属性
• 离散化处理：将连续型的属性变量进行离散化处 理，形成决策树的训练集
– 把需要处理的样本（对应根节点）或样本子集 （对应子树）按照连续变量的大小从小到大进 行排序 – 假设该属性对应的不同的属性值一共有N个， 那么总共有N-1个可能的候选分割阈值点，每个 候选的分割阈值点的值为上述排序后的属性值 中两两前后连续元素的中点 – 用信息增益率选择最佳划分

decisiontreeregressor random_state -回复

decisiontreeregressor random_state -回复

decisiontreeregressor random_state -回复DecisionTreeRegressor是一种基于决策树的回归模型，它可用于预测连续型变量的数值。

在本文中，我们将深入探讨DecisionTreeRegressor模型的原理、使用方法和主要注意事项。

首先，我们来了解一下决策树模型。

决策树是一种树形结构，其中每个内部节点表示特征或属性，每个叶节点表示决策或结果。

在每个内部节点，通过对输入数据的特征进行划分，决策树模型可以逐步推导出所需预测值。

DecisionTreeRegressor是在决策树模型上进行回归分析的一种实现。

决策树回归模型的训练过程是通过对训练样本的反复切割来构建决策树。

切割的依据是最优的特征和切割点，以最大限度地减少预测结果的方差或均方误差。

换句话说，决策树回归模型试图通过选择最佳特征和相应的切割点，将数据划分为不纯度最小的区域。

DecisionTreeRegressor模型中的random_state参数是为了控制模型的随机性。

在训练过程中，决策树模型的切割依据是基于最优特征和切割点的选择。

而这些选择是通过计算不纯度度量值来决定的。

在一些情况下，有多个特征和切割点都能够使得不纯度减少到最小。

在这种情况下，模型可能会因为特征选择的随机性而产生细微的变化。

为了保证模型的可复现性和稳定性，我们可以设置random_state参数的值。

具体来说，设定random_state为一个固定的数值，可以确保每次运行模型时都得到相同的结果。

接下来，让我们看看DecisionTreeRegressor模型的使用方法。

首先，我们需要导入必要的库和模块。

在Python中，我们可以使用sklearn库中的DecisionTreeRegressor类来构建和训练模型。

同时，我们还需要导入一些评价指标，如均方误差（Mean Squared Error）和决定系数（Coefficient of Determination），以评估模型的性能。

工程造价专业外语

工程造价专业外语

Reviewing workLessone 1, 2, 7Cost Planning of Buildings：Chapter 1 and 3course ppt. contentAbbreviations:DBB：Design-Bid-Build设计-招标-建造ACM：Agency Construction Manager代理型项目经理CM-at-Risk：Construction Managers-at-Risk风险型项目经理DB：Design-build设计-建造DBOT：Design-build-Operate-Transfer设计-建造-营运-移交DBOM：Design-build-Operate-Maintain设计-建造-营运-维护DBOOT：Design-build-Own-Operate-Transfer金融-设计-建造-拥有-营运-移交FDBOT：Finance-Design-build-Operate-Transfer 金融-设计-建造-营运-移交‘TPC：Total Project Cost工程总造价CPR：Capital Project Request资本项目需求HV AC：Heating, Ventilating and Air Conditioning采暖通风与空调ISDN：Integrated Services Digital Network综合业务数字网AutoCAD：Auto Computer Aided Design计算机辅助设计RPR：TBM：Tunnel Boring Machine 隧道掘进机BIM：Building Information Modeling建筑信息模型BQs：QS：Quantity Surveyor估算师;估料师WLCC：BS：British standard英国标准CIOB：The Chartered Institute of Building英国皇家特许建造学会ASCE：The American Society of Civil Engineers美国土木工程师学会OGC：NDPBs：BCIS：NAO：Professional terms and expressions:●Physical products:实体产品●Labor organizations：劳工组织Organizations of workers who have banded together to achieve common goals such as protecting the integrity of its trade, achieving higher pay, increasing the number of employees an employer hires, and better working conditions.●Shop drawings：施工图The shop drawing is the manufacturer’s or the contractor’s drawn version of information shown in the construction documents.●Surface topography：表面地形学Surface Topography is the local deviations of a surface from a perfectly flat plane.●Specialty construction contractors：专业建筑承包商●Government regulatory agencies：政府部门的管理机构A government regulatory agency is a government agency responsible for exercising autonomous authority over some area of human activity in a regulatory or supervisory capacity.●Budgetary constraints：预算限制●Modular homes：组合式家居Modular buildings and modular homes are sectional prefabricated buildings, or houses, that consist of multiple sections called modules.●Storm water：雨水Storm water is water that originates during precipitation events. It may also be used to apply to water that originates with snowmelt that enters the stormwater system.●Water distribution systems：配水系统A water distribution system is a system of engineered hydrologic（水文的）and hydraulic （水力的，水压的）components which provide water supply.●Equipment-intensive：装备密集的●Tax exempt revenue：免税收入（exempt：vt.(from)免除，豁免a.被豁免的r evenue：收入）●Working capital：流动资金the money that a business has available for immediate use（capital：此处表资金）●Operating revenues：营业额，营业收入Earnings before interest and taxes●Fast-tracking：边设计边施工Fast-track construction reduces the project time by overlapping the project design and construction phases.●Domino effect：多米诺效应a situation in which one event causes a whole series of events to happen one after the other.●Internal capabilities：内部能力●Appropriation estimates：概算●Contingency allowance：应急准备金The contingency allowance is the money allocated during planning for unscheduled events.●Field Procedure Manual：现场施工程序手册●Upset price：开拍底价●Sketch scheme：初步方案●Iron fist in velvet glove：外柔内刚"iron fist in a velvet glove," referring to a person who appears gentle but is determined and often inflexible underneath.●Capital project: 投资方案●Revenue streams：收入来源、收益流A revenue stream is a form of revenue. Revenue streams refer specifically to the individual methods by which money comes into a company.[citation needed]Revenue streams may be characterized. For example, a revenue stream has volatility, predictability, risk, and return.●Cost parameter：成本参数●Cost optimisation programme：成本优化方案●Performance measurement indicators：绩效考核（衡量）指标An organization may use them to evaluate its success, or to evaluate the success of a particular activity in which it is engaged.●Client brief：设计纲要●Refined cost：细化成本●Decision-tree analysis：决策树分析A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. The decision-tree analysis is the application of it.●Quality of cost：质量成本The Nature/feature/Characteristics of Construction Industry⏹One of the pillar industries of a country国家支柱产业之一⏹Labour-intensive劳动力密集型⏹Capital-intensive (资金密集型)⏹Equipment- intensive设备密集型⏹Long project cycle (工程周期长, time-consuming)⏹Highly restrained（受控制的）by weather conditions and the environmentsince working in the open⏹All projects by contract⏹Teamwork including owner, architects, engineers, contractors (subcontractors ),regulators, material suppliers etc.团队包括业主、建筑师、工程师、承包商(分包方)、监管者、材料供应商等。

CHAPTER引言详解

CHAPTER引言详解
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1989 IJCAI Workshop on Knowledge Discovery in Databases (PiatetskyShapiro)
Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley,
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The book will be covered in two courses at CS, UIUC -(University of Illinois at Urbana-Champaign)
Conferences: Machine learning (ML), AAAI, IJCAI, COLT (Learning Theory), CVPR, NIPS, etc.
Journals: Machine Learning, Artificial Intelligence, Knowledge and Information Systems, IEEE-PAMI, etc.
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决策树(完整)

无缺失值样本中在属性 上取值 的样本所占比例
无缺失值样本中在属性 上取值 的样本所占比例
ቤተ መጻሕፍቲ ባይዱ
谢谢大家！
举例：求解划分根结点的最优划分属性
根结点的信息熵：
用“色泽”将根结点划分后获得3个分支结点的信息熵分别为：
属性“色泽”的信息增益为：
若把“编号”也作为一个候选划分属性，则属性“编号”的信息增益为：
根结点的信息熵仍为：
用“编号”将根结点划分后获得17个分支结点的信息熵均为：
则“编号”的信息增益为：
三种度量结点“纯度”的指标：信息增益增益率基尼指数
1. 信息增益
香农提出了“信息熵”的概念，解决了对信息的量化度量问题。香农用“信息熵”的概念来描述信源的不确定性。
信息熵
信息增益
一般而言，信息增益越大，则意味着使用属性a来进行划分所获得的“纯度提升”越大。决策树算法第8行选择属性
著名的ID3决策树算法
远大于其他候选属性信息增益准则对可取值数目较多的属性有所偏好
2. 增益率
增益率准则对可取值数目较少的属性有所偏好著名的C4.5决策树算法综合了信息增益准则和信息率准则的特点：先从候选划分属性中找出信息增益高于平均水平的属性，再从中选择增益率最高的。
3. 基尼指数
基尼值
基尼指数
著名的CART决策树算法
过拟合：学习器学习能力过于强大，把训练样本自身的一些特点当作了所有潜在样本都会具有的一般性质，导致泛化性能下降。欠拟合：学习器学习能力低下，对训练样本的一般性质尚未学好。
过拟合无法彻底避免，只能做到“缓解”。
不足：基于“贪心”本质禁止某些分支展开，带来了欠拟合的风险
预剪枝使得决策树的很多分支都没有“展开”优点：降低过拟合的风险减少了训练时间开销和测试时间开销

经济博弈论Chapter03_331007283

描求扩证序境。

of play.参无论何时采取行动，参与者都需要考虑他们当前的行动会如何影响未来的行动，包括对手和博它动，指出了博弈的所有可能结果。

决所有的连续决策点。

decision maker in a 博策树。

博节分每一分支都从博弈树上的一个决策点指向另一个决策点或终结点。

terminal node.在一个分支；不过，仅允许有一个分支指向任何一个决将引收益。

终结点并不是所有博弈必需的；一些博弈理论上可以永远进行下去on forever我参Anode is called a 参安均会发生什么？一个名叫卡门的少女正在决定是否要吸烟。

A teenager named Carmen is deciding whether to smoke.首如不一带给她不同的感受和收益。

taste, as well as different payoffs.单门分析一开始，考虑与终结点直接相连的那些行动点。

Start analysis by considering those action nodes that lead directly to terminal nodes.在利在通沿着贯穿整个博弈树的标出的唯一路径，就知道了当所有参与者在正确预测了所有的未来后果下做出最优选择时，这当由The outcome that arises from playing these在在会，甚至根本就没出现！但是，它的可能的出现和潜在的策略在决定今日卡门的招术时发挥了作用。

三每捐但为的所有结果进行赋值（排序）。

可3不多少种？如我均参不However, the equilibrium path of play is complete specification of the rollback equilibrium.它反略得到的。

艾尼塔根在街道花园博弈的反转均衡中，艾米丽得到了最好的结果（the opportunity to make the first move.先不后当这些操纵的招术就是Tactics for such manipulation are第第第第第第第三步：反转求解Step 3: Rollback第一它Slide 47一但一国然对实考有人拿出下。

2023山东省济南市高三三模英语试题含解析

高考针对性训练英语试题本试卷共10页，满分120分。

考试用时100分钟。

注意事项：1.答卷前，考生务必用黑色字迹钢笔或签字笔将自己的姓名、考生号、考场号和座位号填写在答题卡上。

2.作答选择题时，选出每小题答案后，用2B铅笔把答题卡上对应题目选项的答案信息点涂黑;如需改动，用橡皮擦干净后，再选涂其他答案，答案不能答在试卷上。

3.非选择题必须用黑色字迹钢笔或签字笔作答，答案必须写在答题卡各题目指定区域内相应的位置上;如需改动，先划掉原来的答案，然后再写上新的答案;不准使用铅笔和涂改液。

不按以上要求作答的答案无效。

第一部分阅读(共两节，满分50分)第一节(共15小题;每小题2.5分，满分37.5分)阅读下列短文，从每题所给的A、B、C、D四个选项中选出最佳选项。

AAs the forest decreases,Africa has become overly dependent on several tree species.Fortunately,the Volunteer National Parks and Reserves Project is helping to stop this terrible trend.Choose from Kakum National Park,Shai Hills Resource Reserve or Mole National Park and make a vital contribution to environmental preservation in Ghana.Quick factsProgramme:Volunteer Ghana Project Site:National parks&reservesMinimum Duration:4weeks Language Requirement:EnglishMinimum Age:18Accommodations:2meals daily&HomestayWorking Hours:7hours per day Start Dates:Every other FridayYour role as a volunteerYou will help by maintaining the grounds,constructing or repairing park facilities and assisting with land management projects,as well as directing park visitors,promoting the park’s tourist appeal and raising awareness of environmental issues.Also help lower the rate of forest decrease and protect the biodiversity within Ghana’s national parks and reserves.Media-loved volunteers may also get involved in park projects related to video creation,photography andimage arts.National parks and reserves volunteers typically work five days,from Monday to Friday.Working hours are from8a.m.until3p.m...However,schedules and exact duties may vary depending on the park you’re based at, your qualifications,and the time and duration of your stay.Journey to workNational parks and reserves volunteers are placed with a host family living either in the park or in a village nearby.Contact usTo get further information,please visit our website at .Join the Volunteer National Parks and Reserves Project to do your part for the environment in Ghana.1.What does the project offer volunteers?A.Free trips across Ghana.B.Two meals every day.C.A language course.D.A4-week training.2.What does a volunteer’s job involve?A.Helping ground management.B.Building more nature reserves.C.Introducing various tree species.D.Tearing down old park equipment.3.What is the project intended to do?A.Stop animals from dying out.B.Protect Ghana’s environment.C.Keep the diversity of culture.D.Collect ecological information.【答案】1.B 2.A 3.B【解析】【导语】这是一篇应用文。

市场营销 (双语) Chapter 8 Marketing Engineering

Chapter 8 Marketing Engineering
8.1 Origin and Development of the Marketing Engineering
The marketing engineering is a systematic one, which can raise the correctness of the decision making and the benefits of the marketing input by way of integrating marketing knowledge, its data, issues, decision-making models, management systems, information and case banks, etc. so as to help the marketing decision-maker realize the streamlining, quantification, science, and normalization of the decision. Its origin and development have seen the following stages: (1) Application of the mathematic models in the marketing, which is represented by the book “Quantitative Technologies in Analyzing the Marketing” written by R. Frank, etc.; (2) Management information systems (1965). They are mainly embodied in the “Design of the Marketing Neurological Centre for the Enterprise” written by Philip Kotler;
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Fit the input data well and Correctly predict the class labels of records it has never seen before.
A key objective of the learning algorithm is to build
by the model is f11+f00. The total number of incorrect predictions is f10+f01.
9
Confusion matrix
The information in a confusion matrix can be
summarized with the following two measures Accuracy
14
Decision tree construction
Efficient algorithms have been developed to
induce a reasonably accurate, although suboptimal, decision tree in a reasonable amount of time. These algorithms usually employ a greedy strategy that makes a series of locally optimal decisions about which attribute to use for partitioning the data.
x is the attribute set y is the class label, also known as category or target attribute.

The class label is a discrete attribute.
2
Classification
Classification is the task of learning a target
models with good generalization capability.
5
Classification
First, a training set consisting of records
whose class labels are known must be provided. The training set is used to build a classification model. This model is subsequently applied to the test set, which consists of records which are different from those in te construction
A decision tree is grown in a recursive
fashion by partitioning the training records into successively purer subsets. We suppose
11
Decision tree
The series of questions and answers can be
organized in the form of a decision tree. It is a hierarchical structure consisting of nodes and directed edges. The tree has three types of nodes

1
Classification
The input data for a classification task is a
collection of records. Each record, also known as an instance or example, is characterized by a tuple (x, y)
Decision tree classifiers Neural networks Support vector machines

4
Classification
A classification technique employs a learning
algorithm to identify a model that best fits the relationship between the attribute set and the class label of the input data. The model generated by a learning algorithm should
12
Decision tree
In a decision tree, each leaf node is assigned
a class label. The non-terminal nodes, which include the root and other internal nodes, contain attribute test conditions to separate records that have different characteristics.
function f that maps each attribute set x to one of the predefined class labels y. The target function is also known informally as a classification model. A classification model is useful for the following purposes
f11 f 00 Accuracy f11 f10 f 01 f 00

Error rate
Error Rate f10 f 01 f11 f10 f 01 f 00
Most classification algorithms aim at attaining the
Un is the set of training records that are associated with node n. C={c1, c2, ……cK} is the set of class labels.

16
Decision tree construction
If all the records in Un belong to the same class ck,
Detecting spam email messages based on the message header and content. Categorizing cells as malignant or benign based on the results of MRI scans. Classifying galaxies based on their shapes.

Another internal node, at which a new test condition is applied, or A leaf node.
The class label associated with the leaf node is then
assigned to the record.
6
Confusion matrix
Evaluation of the performance of the model is
based on the counts of correctly and incorrectly predicted test records. These counts are tabulated in a table known as a confusion matrix. Each entry fij in this table denotes the number of records from class i predicted to be of class j.
13
Decision tree
Classifying a test record is straightforward once a
decision tree has been constructed. Starting from the root node, we apply the test condition. We then follow the appropriate branch based on the outcome of the test. This will lead us either to
then n is a leaf node labeled as ck. If Un contains records that belong to more than one class,
highest accuracy, or equivalently, the lowest error rate when applied to the test set.
10
Decision tree
We can solve a classification problem by
asking a series of carefully crafted questions about the attributes of the test record. Each time we receive an answer, a follow-up question is asked. This process is continued until we reach a conclusion about the class label of the record.