### 统计代写|机器学习作业代写Machine Learning代考| Data Pre-processing

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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|Feature extraction

Feature extraction, also called feature selection or feature engineering, is one of the most important tasks to find hidden knowledge or business insides. In machine learning, rarely is the whole initial data set that was collected used as input for a learning algorithm. Instead, the initial data set is reduced to a subset of data that is expected to be useful and relevant for the subsequent machine learning tasks. Feature extraction is the process of selecting values from the initial data set. Features are distinctive properties of the input data and are representative descriptive attributes. In literature, there is often a distinction between feature selection and feature extraction [43]. Feature selection means reducing the feature set into a smaller feature set or into smaller feature sets, because not all features are useful for a specific task. Feature extraction means converting the original feature set into a new feature set that can perform a data mining task better or faster. Here, we treat feature extraction and feature selection interchangeably. Feature selection and, as mentioned before, generally data pre-processing is highly domain specific, whereas machine learning algorithms are not. Feature selection is also independent of the machine learning algorithm used.
Features do not need to be directly observable. For instance, a large set of directly observable features might be reduced using dimensionality reduction techniques into a smaller set of indirectly observable features, also called latent variables or hidden variables. Feature extrac-

tion can be seen as a form of dimensionality reduction. Often features are weighted, so that not every feature contributes equally to the result. Usually the weights are presented to the learner in a separate vector.
A sample feature extraction task is word frequency counting. For instance, reviews of a consumer product contain certain words more often if they are positive or negative. A positive review of a new car typically contains words such as “good”, “great”, “excellent” more often than negative reviews. Here, feature extraction means defining the words to count and counting the number of times a positive or negative word is used in the review. The resulting feature vector is a list of words with their frequencies. A feature vector is an n-dimensional vector of numerical features, so the actual word is not part of the feature vector itself. The feature vector can contain the hash of the word as shown in Figure $3.1$, or the word is recognized by its index, the position in the vector.

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

Data sampling is the process of selecting a data subset, since analyzing the whole data set is often too expensive. Sampling reduces the number of instances to a subset that is processed instead of the whole data set. Samples can be selected randomly so every instance has the same probability to be selected. The selection process should guarantee that the sample is representative of the distribution that governs the data, thereby ensuring that results obtained on the sample are close to ones obtained on the whole data set [43].

There are different ways data can be sampled. When using random sampling, each instance has the same probability to be selected. Using random sampling, an instance can be selected several times. If the randomly selected instance is removed from the data set, we end up with no duplicates in the sample.

Another sampling method uses stratification. Stratification is used to avoid overrepresentation of a class label in the sample and provide a sample with an even distribution of labels. For instance, a sample might contain an overrepresentation of male or female instances. Stratification first divides the data in homogeneous subgroups before sampling. The subgroups should be mutually exclusive. Instead of using the whole population, it is divided into subpopulations (stratum), a bin with female and a bin with male instances. Then, an even number of samples is selected randomly from each bin. We end up with a stratified random sample with an even distribution of female and male instances. Stratification is also often used when dividing the data set into a training and testing subset. Both data sets should have an even distribution of the features, otherwise the evaluation of the trained learner might give flawed results.

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

Raw data often needs to be transformed. There are many reasons why data needs to be transformed. For instance, numbers might be represented as strings in a raw data set and need to be transformed into integers or doubles to be included into a feature vector. Another reason might be the wrong unit. For instance, Fahrenheit needs to be converted into Celsius, or inch needs to be converted into metric.

Sometimes the data structure has to be transformed. Data in JSON (JavaScript Object Notation) format might have to be transformed into XML (Extensible Markup Language) format. In this case, data mapping has to be transformed since the metadata might be different in the JSON and XML file. For instance, in the source JSON file names might be denoted by “first name”, “last name” whereas in the XML it is called “given name” and “family name”. Data transformation is almost always needed in one or another form, especially if the data has more than one source.

## 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。