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# 计算机代写|机器学习代写Machine Learning代考|Level the playing field

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## 计算机代写|机器学习代写Machine Learning代考|Level the playing field

For our experimentation to be meaningful with these nine separate approaches, we need to ensure that we’re playing fairly. This means that we’re not only comparing using the same dataset, but also evaluating the test data against the predictions with the exact same error metrics. The core issue that we need to prevent is indecision and chaos among the team when measuring the effectiveness of a solution (which wastes time that, as we’ve mentioned before, we simply don’t have if we want to move to the MVP phase of the project).

Since we’re looking at a time-series problem, we’re going to evaluate a regression problem. We know that, to do a true comparison, we need to control the data splits (which we will explore throughout the code examples in section 5.2), but we also need to agree on an evaluation metric that each model is going to record to do the comparison of goodness of fit of the prediction. Since we’re eventually going to need to build thousands of these models, and the raw prediction values are of wildly different orders of magnitude (just slightly more people fly through JFK and ATL than do through, say, Boise), the team members have agreed to use MAPE as the comparison metric. In a wise decision, though, they have also agreed to capture as many regression metrics as are applicable to a time-series regression problem, should they choose to switch to a different metric during tuning later for the per model optimizations.
For this reason, we’ll agree to collect metrics on MAPE, MAE, MSE, RMSE, explained variance, and R-squared. This way, we’ll have the flexibility to discuss the benefits of the different metrics as they relate to the data and to the project.

## 计算机代写|机器学习代写Machine Learning代考|Performing experimental prep work

After the planning and research phase is completed by a team focused on building an ML solution to a business problem, the next phase, preparation for experimental testing, is one of the most oft-omitted activities in the DS community (speaking from personal experience here). Even with a solid plan of who is going to test what, an agreedupon series of metrics, an evaluation of the dataset, and an agreed-upon methodology of how far into experimentation each team will be going, this preparatory phase, if ignored, will create more inefficiencies that can lead to a project being delayed. This preparatory phase is focused on doing a deep analysis of the datasets, creating common tools that the entire team can use in order to increase the speed at which they can evaluate their experimental attempts.

At this point, we’ve decided on some models to try, set the ground rules for the experimentation phase, and selected our language (Python, mostly because of the statsmodels library) and our platform (Jupyter Notebook running on Docker containers so we don’t waste our time with library compatibility issues and can rapidly prototype tests and see visualizations directly). Before we start firing off a bunch of modeling tests, it’s important to understand the data as it relates to the problem at hand.

For this forecasting project, that means going through a thorough analysis of stationarity tests, a decomposition of the trend, identification of severe outliers, and building basic visualization tooling that will aid in the rapid phases of model testing that the subteams will be doing. As shown in figure 5.4, we’ll cover each of these key stages of preparation work to ensure that each of our hacking teams will have an efficient development process and won’t be focused on creating nine different copies of the same way of plotting and scoring their results.

## 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。