统计代写|机器学习作业代写Machine Learning代考| Normalization, discretization and aggregation

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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|Normalization, discretization and aggregation

Normalization can mean different things in statistics. It can mean transforming data, that has been measured at different scales into a common scale. Using machine learning algorithms, numeric features are often scaled into a range from 0 to 1 . Normalization can also include averaging of values, e.g., calculating the means of a time series of data over specific time periods, such as hourly or daily means. Sometimes, the whole probability distribution is aligned as part of the normalization process.

Discretization means transferring continuous values into discrete values. The process of converting continuous features to discrete ones and deciding the continuous range that is being assigned to a discrete

value is called discretization [43]. For instance, sensor values in a smart building in an Internet of Things (IoT) setting, such as temperature or humidity sensors, are delivering continuous measurements, whereas only values every minute might be of interest. An other example is the age of online shoppers, which are continuous and can be discretized into age groups such as “young shoppers”, “adult shoppers” and “senior shoppers”.

Data aggregation means combining several feature values in one. For instance, going back to our Internet of Things example, a single temperature measurement might not be relevant but the combined temperature values of all temperature sensors in a room might be more useful to get the full picture of the state of a room.

Data aggregation is a very common pre-processing task. Among the many reasons to aggregate data are the lack of computing power to process all values, to reduce variance and noise and to diminish distortion.

统计代写|机器学习作业代写Machine Learning代考|Entity resolution

Entity resolution, also called record linkage, is a fundamental problem in data mining and is central for data integration and data cleaning. Entity resolution is the problem of identifying records that refer to the same real-world entity and can be an extremely difficult process for computer algorithms alone [39]. For instance, in a social media analysis project, we might want to analyse posts of users on different sites. The same user might have the user name “John” on Facebook, “JSmith” on Twitter and “JohnSmith” on Instagram. Here, entity resolution aims to identify the user accounts of the same user across different data sources, which is impossible if only the user names are known. Also, there is the danger that users are confused and the user name “JSmith” is associated with a different user, e.g., “James Smith”. In this case, record disambiguation methods have to be applied. If the data set is large and we have $n$ records, every record has to be compared with all the other records. In the worst case, we have $O\left(n^{2}\right)$ comparisons to compute. We can reduce the amount of comparisons by applying more intelligent comparison rules. For instance, if we have three instances $a$, $b$ and $c$, if $a=b$ and $a \neq c$ we can infer that $b \neq c$. Reducing the number of comparisons can diminish the effort but is not always feasible and a considerable amount of research has been conducted to develop automated, machine-based techniques.

统计代写|机器学习作业代写Machine Learning代考|Entity resolution

As with many pre-processing tasks, we can use clustering methods for entity resolution. In fact, entity resolution is a clustering problem since we group records according to the entity they belong to. It can be addressed similar to data deduplication by finding some similarity measures and then using a distance measure, such as the Eucledian distance or the Jaccard similarity, to find records that belong to the same real-world entity. Clustering techniques are described in more detail in Chapter 6 . In practice, the probability that a record belongs to a certain entity is usually calculated. Entity resolution can also be used for reducing redundancies in data sets and reference matching, where noisy records are linked to clean ones. Active learning methods and semi-supervised techniques have also been used for entity resolution. However, machine-based techniques, despite all the research effort that has been invested, are far from being perfect.

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。