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

Doug I. Jones

Lorem ipsum dolor sit amet, cons the all tetur adiscing elit

如果你也在 怎样代写统计与机器学习Statistical and Machine Learning这个学科遇到相关的难题,请随时右上角联系我们的24/7代写客服。


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|SEC595

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










  • 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:
    • Target/class/label:



  • 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
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™

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。







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。


hurry up

15% OFF

On All Tickets

Don’t hesitate and buy tickets today – All tickets are at a special price until 15.08.2021. Hope to see you there :)