统计代写|统计与机器学习作业代写Statistical and Machine Learning代考|ECE414

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
couryes™为您提供可以保分的包课服务

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

Logistic regressions are closely related to linear regressions. In logistic regression, the expected value of the target is transformed by a link function to restrict its value to the unit interval. In this way, model predictions can be viewed as primary outcome probabilities between 0 and 1 . A linear combination of the inputs generates a logit score, or the log of the odds of the primary outcome, in contrast to linear regression, which estimates the value of the target. The range of logit scores is from negative infinity to positive infinity. For binary prediction, any monotonic function that maps the unit interval to the real number line can be considered as a link. The logit link function is one of the most common. Its popularity is due, in part, to the interpretability of the model.
For example, if you want to use logistic regression for classification of a binary target, you would want to restrict the range of the output to be between 0 and 1 . The logit link function transforms the continuous logit scores into probabilities between 0 and 1. The continuous logit scores, or the logit of $\hat{p}$, is given by the log of the odds, which is the log of the probability of the event divided by the probability of the non-event. This logit transformation transforms the probability scale to the real line of negative infinity to positive infinity. Therefore, the logit can be modeled with a linear combination since linear combinations can take on any value.
The logistic model is particularly easy to interpret because each predictor variable affects the logit linearly. The coefficients are the slopes. Exponentiating each parameter estimate gives the odds ratios, which compares the odds of the event in one group to the odds of the event in another group.

The odds ratio shows the strength of the association between the predictor variable and the target variable. If the odds ratio is 1 , then there is no association between the predictor variable and the target. If the odds ratio is greater than 1 , then the group in the numerator has higher odds of having the event. If the odds ratio is between 0 and 1 , then the group in the denominator has higher odds of having the event. For example, an odds ratio of 3 indicates that the odds of getting the event for the group in the numerator are three times that for the group in the denominator. The group in the numerator and denominator is based on the coding of the input variable. For example, if the parameter estimate for the input variable age is $0.97$, then the exponent is $2.66$. That means, for a one unit increase in age, the odds are increased by $166 \%((2.66-1) * 100)$.

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

An example of a business problem that can be addressed using logistic regression is the probability of debt repayment. The main goal of the model is to rank insolvent customers based on the probability of paying off their unpaid bills. The results of the model can be used to target the insolvent customers who are more likely to pay off their debt. The input variables in the data are:

• Demographic information about the customers
• Payment type, day, frequency of payment, amount paid, and so on
• Payment delay information
• Aging of the customer in the company, product, or service
• Credit history for the customer
• Past delinquent bills for the customer
• Debt to income ratio, debt to bill ratio, total debt to total bill ratio, and so on
• Others
The target variable is:
• Whether the customer has paid off the unpaid bill
Telecommunications companies usually rank insolvent customers by how much they owe or by the age of the unpaid bills. Then, they target the customers with the largest or oldest debt. However, if the telecommunications company ranked the customers based on the probability of payment and targeted the customers with the highest probabilities, there might be an increase in revenue. This problem can cause even more damage in countries with unstable economic situations and high inflation rates. If customers do not pay their bills, the company needs to get money from the financial market to maintain the cash flow. This money costs much more than the company charges the insolvent customers in terms of fees and interest rates. The longer the customers remain insolvent, more money needs to be collected from the financial market. The main goal for this model is to allow companies to anticipate cash by contacting customers who are likely to pay their unpaid bills first.

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|逻辑回归

Logistic回归与线性回归密切相关。在逻辑回归中，目标的期望值通过链接函数进行变换，将其值限制在单位区间内。这样，模型预测可以被视为0到1之间的主要结果概率。输入的线性组合产生一个logit分数，或主要结果的概率的对数，这与估计目标值的线性回归相反。logit分数的范围是从负无穷大到正无穷大。对于二元预测，任何将单位区间映射到实数线的单调函数都可以被认为是一个链接。logit链接函数是最常见的函数之一。它的流行部分是由于模型的可解释性。例如，如果您想使用逻辑回归对二进制目标进行分类，您可能希望将输出的范围限制在0到1之间。logit链接函数将连续的logit分数转换为0到1之间的概率。连续的logit分数，或者$\hat{p}$的logit，是由概率的对数给出的，它是事件的概率除以非事件的概率的对数。这个logit变换将概率尺度转化为从负无穷到正无穷的实数直线。因此，logit可以用线性组合建模，因为线性组合可以取任何值。逻辑模型特别容易解释，因为每个预测变量都对logit有线性影响。系数是斜率。对每个参数估计求幂得到比值比，它将一组事件发生的概率与另一组事件发生的概率进行比较

统计代写|统计与机器学习作业代写统计与机器学习代考|收集预测模型

• 客户的人口统计信息
• 付款类型、日期、付款频率、支付金额等
• 付款延迟信息
• 客户在公司、产品或服务中的年龄
• 客户的信用记录
• 客户过去的拖欠账单
• 债务收入比、债务账单比、债务账单比，
• 其他
目标变量是:
• 客户是否已付清未付账单
电信公司通常根据欠款的多少或未付账单的年龄对资不抵债的客户进行排名。然后，他们把目标锁定那些债务最大或最久的客户。但是，如果通信公司根据支付概率对客户进行排名，并锁定可能性最高的客户，可能会增加收入。在经济形势不稳定、通货膨胀率高的国家，这个问题可能造成更大的破坏。如果客户不支付他们的账单，公司需要从金融市场获得资金来维持现金流。这笔钱的成本远远高于公司向资不抵债客户收取的费用和利率。客户资不抵债的时间越长，需要从金融市场回收的资金就越多。该模型的主要目标是让公司通过联系那些可能首先支付未付账单的客户来预测现金

有限元方法代写

tatistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

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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