# 统计代写|统计与机器学习作业代写Statistical and Machine Learning代考|SEC595

#### 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|Feature Selection

Data scientists usually deal with hundreds of predictor variables. This limits the ability to explore and model the relationships among the variables since as the dimensions increase, the data becomes sparse. The large number of predictor variables leads to the curse of dimensionality, which means the complexity of the data set increases rapidly with the increased number of variables. The bottom line is the amount of data a data scientist needs increases exponentially as the number of variables increase.
The remedy to the curse of dimensionality is feature selection, also known as dimension reduction. In other words, data scientists want to eliminate irrelevant and redundant variables without inadvertently eliminating important ones. Some of the dimension reduction methods are correlation analysis, regression analysis, and variable clustering.

When features are created, a common recommendation is to eliminate the original variables that were used to construct the features because the features and the original variables will probably be redundant. For example, if the log of age is created, the original age variable is eliminated. However. ones survive to the final model. In this way, data scientists see whether age or the log of age survives to the final model.
Redundancy among predictor variables is an unsupervised concept since it does not involve the target variable. On the other hand, the relevancy of a variable considers the relationship between the predictor variable and the target variable. In high-dimensional data sets, identifying irrelevant variables is more difficult than identifying redundant variables. A good strategy is to first reduce redundancy and then tackle irrelevancy in a lower dimension space.

A redundant variable does not give any new information that was not already explained by other variables. For example, knowing the value of input household income usually is a good indication of home value. As one value increases, the other value also increases.
An irrelevant variable does not provide information about the target. For example, if the target is whether you gave to a charitable organization, predictions change with the predictor variable response to previous solicitations, but not with the predictor variable show size.

Figure $2.14$ shows a data set with numerous variables such as the original variables, derived variables, transformed variables, variables obtained from text mining, variables obtained from principal component analysis, variables obtained from autoencoder data transformation, and the variables obtained from social network analysis. The goal is to reduce the number of variables down to a reasonable number while still maintaining a high predictive accuracy for the model. It should be noted that social network analysis is discussed in a later chapter.

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

Classification and estimation are common types of predictive models. Classification assumes that the target is a class variable. The target can be a binary class, 0 or 1 , yes or no, or it can be a multinomial class, like $1,2,3,4$, and 5 , or low, medium, and high. For example, is this business event a fraudulent transaction (yes or no)? Estimation assumes that the target is a continuous number. The target can take any value in the range of negative infinity to positive infinity.
Both predictive models, classification and estimation, require the following:

• Observations/cases/instances:
• A real case comprising a set of attributes that describe the observation.
• Inputs/attributes/variables:
o The measures of the observation, or its attributes. It can be demographic information about the customer, such as age, salary, and marital status.
• Target/class/label:
o A tag or label for each observation. Default or no default is an example.
The data used to train a supervised machine learning model consists of a set of cases or observations that happened in the past, which means the value of the target is known. The main idea is to find relationships between the predictor variables and the target. For example, looking at all churn cases that happened in the past six months, what input variables can explain whether the event of churn and non-churn occurred? If a model can be trained based on past cases and find what input variables can explain the churn and the non-churn, then this model can be used to predict cases of churn in the future.

A statistical model maps the set of input variables to the target. The model tries to create a concise representation of the inputs and the target. It tries to capture the relationship between the inputs and the target. For example, when the target is a binary target such as churn versus no churn, the model tries to explain, based on the input variables, whether the customer is willing to make churn or not. What input variables are associated to the yes (churn) and what values? What variables are associated to the no (no churn) and what values? The target is the outcome to be predicted. The cases are the units on which the prediction is made.

For example, imagine a company selling various products to its customers over time. A sample of customers can be identified with some important characteristics, including:

• Age
• Gender
• Average revenue generated over the years
• How long the customer has been in the database
• Number of products purchased
• Marital status
• If the customer is a homeowner
• How many children the customer has

# 统计与机器学习代考

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## 统计代写|统计与机器学习作业代写统计与机器学习代考|分类与估计

• 观察/案例/实例:
• 一个由一组描述观察结果的属性组成的真实案例
• input /attributes/variables:
o观察结果的度量，或其属性。它可以是关于客户的人口统计信息，例如年龄、工资和婚姻状况。
• Target/class/label:
o用于每个观察的标记或标签。默认或不默认就是一个例子。用于训练有监督机器学习模型的数据由过去发生的一组案例或观察组成，这意味着目标的值是已知的。主要思想是找到预测变量和目标之间的关系。例如，查看过去6个月发生的所有流失案例，哪些输入变量可以解释是否发生了流失和非流失事件?

• 年龄
• 性别
• 年平均收入
• 客户在数据库中存在的时间
• 购买的产品数量
• 婚姻状况
• 如果客户是有房子的
• 客户有多少孩子

## 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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