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

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。

我们提供的统计与机器学习Statistical and Machine Learning及其相关学科的代写,服务范围广, 其中包括但不限于:

  • Statistical Inference 统计推断
  • Statistical Computing 统计计算
  • Advanced Probability Theory 高等楖率论
  • Advanced Mathematical Statistics 高等数理统计学
  • (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
统计代写|统计与机器学习作业代写Statistical and Machine Learning代考|ECE6254

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.

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




  • 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Statistical and Machine Learning代考 请认准statistics-lab™

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术语 广义线性模型(GLM)通常是指给定连续和/或分类预测因素的连续响应变量的常规线性回归模型。它包括多元线性回归,以及方差分析和方差分析(仅含固定效应)。



有限元是一种通用的数值方法,用于解决两个或三个空间变量的偏微分方程(即一些边界值问题)。为了解决一个问题,有限元将一个大系统细分为更小、更简单的部分,称为有限元。这是通过在空间维度上的特定空间离散化来实现的,它是通过构建对象的网格来实现的:用于求解的数值域,它有有限数量的点。边界值问题的有限元方法表述最终导致一个代数方程组。该方法在域上对未知函数进行逼近。[1] 然后将模拟这些有限元的简单方程组合成一个更大的方程系统,以模拟整个问题。然后,有限元通过变化微积分使相关的误差函数最小化来逼近一个解决方案。





随机过程,是依赖于参数的一组随机变量的全体,参数通常是时间。 随机变量是随机现象的数量表现,其时间序列是一组按照时间发生先后顺序进行排列的数据点序列。通常一组时间序列的时间间隔为一恒定值(如1秒,5分钟,12小时,7天,1年),因此时间序列可以作为离散时间数据进行分析处理。研究时间序列数据的意义在于现实中,往往需要研究某个事物其随时间发展变化的规律。这就需要通过研究该事物过去发展的历史记录,以得到其自身发展的规律。


多元回归分析渐进(Multiple Regression Analysis Asymptotics)属于计量经济学领域,主要是一种数学上的统计分析方法,可以分析复杂情况下各影响因素的数学关系,在自然科学、社会和经济学等多个领域内应用广泛。


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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