### 机器学习代写|自然语言处理代写NLP代考|PREPARING DATASETS

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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

## 机器学习代写|自然语言处理代写NLP代考|Discrete Data Versus Continuous Data

As a simple rule of thumb: discrete data is a set of values that can be counted, whereas continuous data must be measured. Discrete data can reasonably fit in a drop-down list of values, but there is no exact value for making such a determination. One person might think that a list of 500 values is discrete, whereas another person might think it’s continuous.

For example, the list of provinces of Canada and the list of states of the United States are discrete data values, but is the same true for the number of countries in the world (roughly 200 ) or for the number of languages in the world (more than 7,000$)$ ?

Values for temperature, humidity, and barometric pressure are considered continuous. Currency is also treated as continuous, even though there is a measurable difference between two consecutive values. The smallest

unit of currency for U.S. currency is one penny, which is $1 / 100$ th of a dollar (accounting-based measurements use the “mil,” which is $1 / 1,000$ th of a dollar).
Continuous data types can have subtle differences. For example, someone who is 200 centimeters tall is twice as tall as someone who is 100 centimeters tall; the same is true for 100 kilograms versus 50 kilograms. However, temperature is different: 80 degrees Fahrenheit is not twice as hot as 40 degrees Fahrenheit.

Furthermore, keep in mind that the meaning of the word “continuous” in mathematics is not necessarily the same as continuous in machine learning. In the former, a continuous variable (let’s say in the 2D Euclidean plane) can have an uncountably infinite number of values. A feature in a dataset that can have more values than can be reasonably displayed in a drop-down list is treated as though it’s a continuous variable.

For instance, values for stock prices are discrete: they must differ by at least a penny (or some other minimal unit of currency), which is to say, it’s meaningless to say that the stock price changes by one-millionth of a penny. However, since there are so many possible stock values, it’s treated as a continuous variable. The same comments apply to car mileage, ambient temperature, and barometric pressure.

## 机器学习代写|自然语言处理代写NLP代考|“Binning” Continuous Data

Binning refers to subdividing a set of values into multiple intervals, and then treating all the numbers in the same interval as though they had the same value.

As a simple example, suppose that a feature in a dataset contains the age of people in a dataset. The range of values is approximately between 0 and 120 , and we could bin them into 12 equal intervals, where each consists of 10 values: 0 through 9,10 through 19,20 through 29 , and so forth.

However, partitioning the values of people’s ages as described in the preceding paragraph can be problematic. Suppose that person A, person B, and person C are 29,30 , and 39 , respectively. Then person $A$ and person $B$ are probably more similar to each other than person $B$ and person C, but because of the way in which the ages are partitioned, $B$ is classified as closer to $C$ than to A. In fact, binning can increase Type I errors (false positive) and Type II errors (false negative), as discussed in this blog post (along with some alternatives to binning):

As another example, using quartiles is even more coarse-grained than the earlier age-related binning example. The issue with binning pertains to the consequences of classifying people in different bins, even though they are in close proximity to each other. For instance, some people struggle financially because they earn a meager wage, and they are disqualified from financial assistance because their salary is higher than the cutoff point for receiving any assistance.

## 机器学习代写|自然语言处理代写NLP代考|Scaling Numeric Data via Normalization

A range of values can vary significantly, and it’s important to note that they often need to be scaled to a smaller range, such as values in the range $[-1,1]$ or $[0,1]$, which you can do via the tanh function or the sigmoid function, respectively.

For example, measuring a person’s height in terms of meters involves a range of values between $0.50$ meters and $2.5$ meters (in the vast majority of cases), whereas measuring height in terms of centimeters ranges between 50 centimeters and 250 centimeters: these two units differ by a factor of 100 . A person’s weight in kilograms generally varies between 5 kilograms and 200 kilograms, whereas measuring weight in grams differs by a factor of 1,000 . Distances between objects can be measured in meters or in kilometers, which also differ by a factor of 1,000 .

In general, use units of measure so that the data values in multiple features belong to a similar range of values. In fact, some machine learning algorithms require scaled data, often in the range of $[0,1]$ or $[-1,1]$. In addition to the tanh and sigmoid function, there are other techniques for scaling data, such as standardizing data (think Gaussian distribution) and normalizing data (linearly scaled so that the new range of values is in $[0,1]$ ).

The following examples involve a floating point variable $x$ with different ranges of values that will be scaled so that the new values are in the interval $[0,1]$.

• Example 1: If the values of $x$ are in the range $[0,2]$, then $x / 2$ is in the range $[0,1]$.
• Example 2: If the values of $x$ are in the range $[3,6]$, then $x-3$ is in the range $[0,3]$, and $(x-3) / 3$ is in the range $[0,1]$.
• Example 3: If the values of $x$ are in the range $[-10,20]$, then $x+10$ is in the range $[0,30]$, and $(x+10) / 30$ is in the range of $[0,1]$.

## 机器学习代写|自然语言处理代写NLP代考|Scaling Numeric Data via Normalization

• 示例 1：如果X在范围内[0,2]， 然后X/2在范围内[0,1].
• 示例 2：如果X在范围内[3,6]， 然后X−3在范围内[0,3]， 和(X−3)/3在范围内[0,1].
• 示例 3：如果X在范围内[−10,20]， 然后X+10在范围内[0,30]， 和(X+10)/30是在范围内[0,1].

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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。