### 统计代写|机器学习作业代写Machine Learning代考| Outlier removal

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• 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

## 统计代写|机器学习作业代写Machine Learning代考|Outlier removal

Outlier removal is another common data pre-processing task. An outlier is an observation point that is considerably different from the other instances. Some machine learning techniques, such as logistic regression, are sensitive to outliers, i.e., outliers might seriously distort the result. For instance, if we want to know the average number of Facebook friends of Facebook users we might want to remove prominent people such as politicians or movie stars from the data set since they

typically have many more friends than most other individuals. However, if they should be removed or not depends on the aim of the application, since outliers can also contain useful information.

Outliers can also appear in a data set by chance or through a measurement error. In this case, outliers are a data quality problem like noise. However, in a large data set outliers are to be expected and if the number is small, they are usually not a real problem. Clustering is often used for outlier removal. Outliers can also be detected and removed visually, for instance, through a scatter plot, or mathematically, for instance, by determining the $z$-score, the standard deviations by which the outlier is above the mean value of the data set.

## 统计代写|机器学习作业代写Machine Learning代考|Data deduplication

Duplicates are instances with the exact same features. Most machine learning tools will produce different results if some of the instances in the data files are duplicated, because repetition gives them more influence on the result [40]. For example, Retweets are Tweets posted by a user that is not the author of the original Tweet and have the exact same content as the original Tweet except for metadata such as the timestamp of when it has been posted and the user who posted, retweeted, it. As with outliers, if duplicates should be removed or not depends on the context of the application. Duplicates are usually easily detectable by simple comparison of the instances, especially if the values are numeric, and machine learning frameworks often offer data deduplication functionality out of the box. We can also use clustering for data deduplication since many clustering techniques use similarity metrics and they can be used for instance matching based on similarities.

## 统计代写|机器学习作业代写Machine Learning代考| Relevance filtering

Relevance filtering typically happens at different stages of a machine learning project. Data deduplication can be considered a relevance filtering step if every instance has to be unique. Feature selection can also be considered relevance filtering since relevant features are sep-

arated from irrelevant ones. Stop words removal in text analysis is a relevance filtering procedure since irrelevant words or signs such as smileys are removed. Many natural language processing frameworks offer stop words removal functionality. Stop words are usually the most common words in a language such as “the”, “a”, or “that”. However, the list often needs to be adjusted since a stop word might be relevant, for instance, in a name such as “The Beatles”.

Since feature selection can be considered a search problem, using different search filters can be used to combat noise. For instance, people often enter fake details when entering personal data, such as fake addresses or phone numbers, since they do not want to be contacted by a call center. These fake profiles need to be filtered out otherwise they can negatively influence the predictive performance of a learner. Often this already happens when data is collected by using queries that omit irrelevant or fake data.

Relevance filtering can also happen after the features have been selected. Different features often do not contribute equally to the result. Some features might not contribute at all and can be filtered out. Data mining tools usually provide filter functionality at the feature level so learners can be trained on different feature sets.

## 广义线性模型代考

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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。