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# 计算机代写|机器学习代写Machine Learning代考|BASELINE COMPARISON VISUALIZATION

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## 计算机代写|机器学习代写Machine Learning代考|BASELINE COMPARISON VISUALIZATION

To have a basic reference for more-complex models, it can be beneficial to see what the simplest implementation produces; then we can see if whatever we come up with can do better than that. This baseline, for the purposes of time-series modeling, can take the form of a simple moving average and an exponentially smoothed average. Neither of these two approaches would be applicable for the forecasting needs of the project, but their output results can be used to see, within the holdout period for validation, if our more sophisticated approaches will be an improvement.

To create a visualization that the teams can use to see these relationships for simpler algorithms, we first have to define an exponential smoothing function, as shown in the next listing. Keep in mind that this is all designed both to standardize the work of each team and to build an effective communication tool for conveying the success of the project to the business.

A complementary function is needed for additional analytics purposes to generate a metric and error estimation for these simple modeling fits for the time series. The following listing provides a method for calculating the mean absolute error for the fit, as well as for calculating the uncertainty intervals (yhat values).

## 计算机代写|机器学习代写Machine Learning代考|STANDARD METRICS

The last thing the team needs to implement before moving to the model experimentation is the standardized measurement of the forecasting predictions to holdout validation data. This effort is to eliminate any chance of debate regarding the effectiveness of each implementation. We’re effectively streamlining the adjudication of each implementation’s merits by way of standardization, which will not only save time in meetings, but also provide a strong scientific methodology to the comparison of each.

If were we to leave each team to determine its own optimal evaluation metrics, comparing them to one another would be nigh impossible, leading to rework of tests and further project delays. If we were to accumulate enough of these avoidable delays, we could dramatically increase the possibility of project abandonment.

Arguing over metrics sounds silly, right?
Yes. Yes, it most certainly does.
Have I seen it done? Yes, I have.
Have I done it? Shamefully, yes, and I wish I had those hours of my life back to use more fruitfully.
Have I endured it as a recipient? I most certainly have.
Have I seen it be the cause of a project being cancelled? No, that’s ridiculous.
What needs to be mentioned is that time is finite. When building a solution to solve a business problem, only so many delays can be allowed to occur before the business unit will either continue doing what it’s been doing up until the DS team was involved, or will flat-out call for a cancellation of the project and basically refuse to ever work with the team again.

Avoidable and superfluous delays surrounding sustained arguments about which metric to use to evaluate a model are flat-out silly, particularly when we consider that it’s such a trivial investment of time to calculate all the metrics for a model evaluation and have their referenceable scores preserved for post hoc evaluation at any time in the future. Just collect all that are relevant to the problem you’re trying to solve (with the notable exception mentioned earlier-if the metric is of such computational complexity to prove noticeably expensive to acquire, make sure it’s worthwhile to capture before writing the code for it). Adapting the code to support such flexibility is in alignment with Agile principles, permitting a rapid pivot without requiring a large refactoring to change the functionality.

## 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。