统计代写|统计与机器学习作业代写Statistical and Machine Learning代考|ECE6254

Doug I. Jones

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couryes-lab™ 为您的留学生涯保驾护航 在代写统计与机器学习Statistical and Machine Learning方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写统计与机器学习Statistical and Machine Learning方面经验极为丰富，各种代写机器学习Statistical and Machine Learning相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础
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统计代写|统计与机器学习作业代写Statistical and Machine Learning代考|Decision Tree

Decision trees are statistical models designed for supervised prediction problems. Supervised prediction encompasses predictive modeling, pattern recognition, discriminant analysis, multivariate function estimation, and supervised machine learning. A decision tree includes the following components:

• An internal node is a test on an attribute.
• A branch represents an outcome of the test, such as color=purple.
• A leaf node represents a class label or class label distribution.
• At each node, one attribute is chosen to split the training data into distinct classes as much as possible.
• A new instance is classified by following a matching path to a leaf node.
The model is called a decision tree because the model can be represented in a tree-like structure. A decision tree is read from the top down starting at the root node. Each internal node represents a split based on the values of one of the inputs. The inputs can appear in any number of splits throughout the tree. Cases move down the branch that contains its input value. In a binary tree with interval inputs, each internal node is a simple inequality. A case moves left if the inequality is true and right otherwise. The terminal nodes of the tree are called leaves. The leaves represent the predicted target. All cases reaching a leaf are given the same predicted value. The leaves give the predicted class as well as the probability of class membership.

Decision trees can also have multi-way splits where the values of the inputs are partitioned into disjoint ranges.
When the target is categorical, the model is called a classification tree. A classification tree can be thought of as defining several multivariate step functions. Each function corresponds to the posterior probability of a target class. When the target is continuous, the model is a called a regression tree. The leaves give the predicted value of the target. All cases that reach a leaf are assigned the same predicted value. Cases are scored using prediction rules. These prediction rules define the regions of the input space in which the predictions are made. Each prediction rule tries to make the region of the input space purer with regard to the target response value.
To illustrate decision trees using business data, a generic data set containing information about payment is used with a binary target of default. For simplicity, the input variables are:

• Previous delay: the number of previous delays since the time analyzed.
• Over billing: the billing amount difference, or the billing amount divided by average billing amount.
• Aging: the time since the customer first started consuming products or services from the company.

统计代写|统计与机器学习作业代写Statistical and Machine Learning代考|Subscription Fraud

A business problem where decision tree models can be useful is subscription fraud. In telecommunications, subscription fraud is when a fraudster uses a stolen or a synthetic identity to acquire mobile devices and services with no intention to pay. In many countries, telecommunications regulations allow customers to remain insolvent for a period without getting their services blocked. This causes major financial damages to the companies. Subscription fraud in telecommunications can be even worse as the proceeds and services are sometimes used by organized crime and terrorist networks. The main goal of the model is to detect subscription fraud and to prevent intentional bad debts. Fraud analysts need to be careful when assessing the cases to avoid adversely impacting the customer journey for the genuine customers. Blocking genuine communication services by mistake is a genuine problem.
As shown in Figure 3.3, a usual framework involving fraud – either subscription fraud or usage fraud – consists of a customer relationship management (CRM) system to receive customers’ orders. These orders are evaluated by a credit system (it can be accomplished using a credit bureau). In parallel, these orders can also be analyzed by a subscription fraud system, which normally receives information about past customers’ transactions. For example, in telecommunications, all raw transactions (calls or even calls attempted) are fetched by the collection systems. This system sends all transactions to a mediation system to aggregate all information and filter the billable transactions. These billable transactions are sent to the billing systems, which process the bills and charge the customers. All this information, in different levels, are used to evaluate and detect subscription and usage fraud. Historical customer information and transaction information are gathered in the data warehouse, which provides the data needed by the data mining tool, environment, or system to train, evaluate, and deploy the predictive models.When a service order is placed in the call center (CRM), the service representative must decide in a matter of seconds whether the request is a fraudulent event or a genuine request. This can be accomplished using decision tree models that recognize patterns associated with subscription fraud. Some of the information used could include stolen identities, fake addresses, specific payment methods, and known blocked lists. The models are used to compute the probability of subscription fraud, and these scores are relayed to the service representative. With this information, the service representative can decide whether this is subscription fraud or a genuine customer. As the decision tree models are fit because of a set of rules based on thresholds, some of the rules that are the most correlated to the subscription fraud can be communicated to the representative when evaluating the case. The list of rules generated by the decision tree is especially useful to the service representative during the customer call but also to the team of fraud analysts when analyzing the cases afterward. Some of the high probability subscription fraud cases might go through during the customer service call, but fraud analysts can evaluate cases afterward to decide what actions to take on some of the orders.

统计与机器学习代考

统计代写|统计与机器学习作业代写统计与机器学习代考|决策树

• 内部节点是对属性的测试。分支表示测试的结果，如color=purple。
• 叶节点表示一个类标签或类标签分布。
• 在每个节点上选择一个属性，尽可能地将训练数据划分为不同的类。
• 新实例按照匹配路径到叶节点进行分类。该模型被称为决策树，因为该模型可以用树状结构表示。从根节点开始从上到下读取决策树。每个内部节点表示基于一个输入值的分割。输入可以出现在整个树的任意数量的分段中。case向下移动包含其输入值的分支。在具有区间输入的二叉树中，每个内部节点都是一个简单的不等式。如果不等式成立，则情况向左移动，反之则向右移动。树的终端节点称为叶。叶子代表预测的目标。所有到达叶的情况都给出相同的预测值。叶子给出了预测的类以及类成员的概率。

• 先前延迟:自分析时间以来的先前延迟次数。
• 过计费:计费金额差额，即计费金额除以平均计费金额。
• 老化:从客户开始消费公司的产品或服务的时间
统计代写|统计与机器学习作业代写统计和机器学习代考|订阅欺诈
决策树模型可以发挥作用的一个业务问题是订阅欺诈。在电信领域，订阅欺诈是指骗子使用窃取的或合成的身份获取移动设备和服务，但无意付费。在许多国家，电信条例允许客户在一段时间内保持资不偿清的状态，而不会使他们的服务受阻。这给这些公司造成了重大的经济损失。电信行业的订阅欺诈可能更严重，因为收益和服务有时被有组织犯罪和恐怖主义网络利用。该模型的主要目标是发现认购欺诈和防止故意坏账。欺诈分析师在评估案例时需要谨慎，以避免对真正客户的客户旅程产生不利影响。错误地阻止真正的通信服务是一个真正的问题。如图3.3所示，一个涉及欺诈(订阅欺诈或使用欺诈)的通常框架由一个客户关系管理(CRM)系统组成，用于接收客户的订单。这些订单由信用系统进行评估(可以使用信用局完成)。与此同时，这些订单也可以通过订阅欺诈系统进行分析，该系统通常会接收过去客户的交易信息。例如，在电信中，所有原始事务(调用或尝试的调用)都由收集系统获取。该系统将所有事务发送到中介系统，以聚合所有信息并过滤可计费事务。这些可计费的事务被发送到账单系统，该系统处理账单并向客户收费。所有这些不同级别的信息都用于评估和检测订阅和使用欺诈。在数据仓库中收集历史客户信息和事务信息，数据仓库提供数据挖掘工具、环境或系统所需的数据，用于训练、评估和部署预测模型。当在呼叫中心(CRM)中放置服务订单时，服务代表必须在几秒钟内确定该请求是欺诈事件还是真实请求。这可以通过使用识别与订阅欺诈相关模式的决策树模型来实现。使用的一些信息可能包括被盗的身份、假地址、特定的支付方式和已知的屏蔽列表。这些模型用于计算订阅欺诈的概率，并将这些分数传递给服务代表。有了这些信息，服务代表就可以确定这是订阅欺诈还是真正的客户。由于决策树模型是适合的，因为有一组基于阈值的规则，因此在评估案例时，可以将一些与认购欺诈最相关的规则传达给代表。决策树生成的规则列表对服务代表在与客户通话期间非常有用，对欺诈分析团队在事后分析案例时也非常有用。一些高概率的订阅欺诈案例可能在客户服务电话期间发生，但欺诈分析师可以在事后评估案例，以决定对某些订单采取什么行动

有限元方法代写

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MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

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