assignmentutor™您的专属作业导师

assignmentutor-lab™ 为您的留学生涯保驾护航 在代写机器学习 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。我们的专家在代写机器学习 machine learning代写方面经验极为丰富，各种代写机器学习 machine learning相关的作业也就用不着说。

• 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

计算机代写|机器学习代写machine learning代考|Temporal Features

Temporal features may make excellent predictors in various settings. Outcomes such as ratings, clicks, purchases (etc.) are often influenced by factors such as the day of the week, the season, or long-term trends that span several years.

Let us explore an example in which we try to predict the rating of a book on Goodreads based on the day of the week that it was entered. Average ratings for each weekday ${ }^{12}$ are shown in Figure 2.11,

As before, we might try to describe this relationship using a line, that is, to fit a model of the form
$$\text { rating }=\theta_0+\theta_1 \times(\text { day of week }) .$$
For this equation to make sense, we need to map the day of the week to a numeric quantity. A trivial encoding might assign numbers sequentially, for example,
$$\text { Sunday }=1 ; \quad \text { Monday }=2 ; \quad \text { Tuesday }=3 ; \quad \text { etc. }$$
Fitting Equation (2.46) using this representation yields the line of best fit depicted in Figure 2.11, which reveals a slight upward trend as the days of the week progress.

The linear trend in Figure $2.11$ seems a fairly poor fit to the data; we might think about fitting a more complex function (like a polynomial) to better capture the observed data. But consider that our model is essentially periodic: Sunday (represented by a 1) follows Saturday (represented by a 7), though we could just as easily have represented Wednesday as 1 and Tuesday as 7 . These choices seem arbitrary, but impact our model in unexpected ways.

This point is perhaps clearer if we visualize our model’s predictions over a period of two weeks, as in Figure 2.12: an encoding of the form in Equation (2.47) corresponds to an unrealistic ‘sawtooth’ pattern that repeats every week.

计算机代写|机器学习代写machine learning代考|Transformation of Output Variables

Finally, just as we saw how to transform features in Section 2.3.1, we can also transform our output variables.

For example, let us consider fitting a model to determine whether resubmitted posts on reddit (Lakkaraju et al., 2013) receive lower numbers of upvotes, that is,
$$\text { upvotes }=\theta_0+\theta_1 \times(\text { submission number })$$
(where the ‘submission number’ is ‘ 1 ‘ for an original submission, ‘ 2 ‘ for the first resubmission, etc.). This model, along with the observations on which it is based, are shown in Figure $2.13$ (left).

Although the line of best fit indicates a slight downward trend, it does not appear to correspond closely to the overall shape of the data. Eye-balling the data in Figure $2.13$, we might hypothesize that the data follows an exponentially decreasing trend, for example, every time you resubmit a post, you can expect to receive half as many upvotes.

Again, one might assume that this type of trend is something that cannot be captured by a linear model. But in fact we can possibly address this by transforming the output variable $y$. For example, consider fitting
$$\log _2 \text { (upvotes) }=\theta_0^{\prime}+\theta_1^{\prime} \text { (submission number). }$$
Now, a unit change in the prediction corresponds to a post receiving twice as many upvotes. While this is still a linear model, the model corresponds to fitting
$$\text { upvotes }=2^{\theta_0^{\prime}+\theta_1^{\prime} \text { (submission number) }} \text {. }$$
The transformed data and line of best fit are shown in Figure $2.13$ (right).
Arguably, this second line better captures the overall trend, and does not have the same issues with outliers. If we transform the fitted values from Equation (2.51) back to their original scale via Equation (2.52), the transformed values actually have a MSE about $10 \%$ lower than the model from Equation (2.50), indicating that the transformed data more closely follows a linear trend compared to the untransformed data.

机器学习代考

计算机代写|机器学习代写machine learning代考|Temporal Features

.

$$\text { rating }=\theta_0+\theta_1 \times(\text { day of week }) .$$

$$\text { Sunday }=1 ; \quad \text { Monday }=2 ; \quad \text { Tuesday }=3 ; \quad \text { etc. }$$

计算机代写|机器学习代写机器学习代考|输出变量转换

.输出变量转换

$$\text { upvotes }=\theta_0+\theta_1 \times(\text { submission number })$$
(其中“提交数”为“1”表示首次提交，“2”表示首次重新提交，等等)。该模型及其所基于的观察结果如图$2.13$(左)所示

$$\log _2 \text { (upvotes) }=\theta_0^{\prime}+\theta_1^{\prime} \text { (submission number). }$$

$$\text { upvotes }=2^{\theta_0^{\prime}+\theta_1^{\prime} \text { (submission number) }} \text {. }$$

有限元方法代写

assignmentutor™作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

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。

assignmentutor™您的专属作业导师
assignmentutor™您的专属作业导师