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assignmentutor-lab™ 为您的留学生涯保驾护航 在代写统计与机器学习Statistical and 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。我们的专家在代写统计与机器学习Statistical and Machine Learning方面经验极为丰富，各种代写机器学习Statistical and 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
• Advanced Probability Theory 高等楖率论
• Advanced Mathematical Statistics 高等数理统计学
• (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|统计与机器学习作业代写Statistical and Machine Learning代考|Model the Data

The fourth step is the analytical model development itself. Some say that this is the most important part, or at least, where data scientists have more fun. Here they will use their creativity and innovation skills to try out multiple analytical approaches to solve the business problem. As stated before, data science is a mix of science and art. This step is the time when data scientists apply both the science behind all algorithms and the art behind all analytical approaches.
Some questions to consider at this phase are:

• Which model had the highest predictive accuracy?
• Which model best generalizes to new data?
• Is it possible to validate the model? Is it possible to test the model? Is it possible to honestly test the models on new data?
• Which model is the most interpretable?
• Which model best explains the correlation between the input variables and the target? Which one best describes the effects of the predictors to the estimation?
• Which model best addresses the business goal?
This is the data scientists’ playground, where they use different algorithms, techniques, and distinct analytical approaches! Yes, a lot of the modeling process involves simply trying new algorithms and evaluating the results. Data science differs from some exact sciences, like math and physics, where based on a robust equation and inputs, it is possible to predict the output. In data science, the set of inputs might be known, but the exact subset of predictors is still unknown until the end of the model training. The equation is created during the model training according to the input data. Then the results are revealed. Any change in the input data set implies a change in the output. Therefore, data science is very much tied to the statistical and mathematical algorithms. However, all the rest is art. Furthermore, many models are not robust as they should be. Some models or algorithms are very unstable, which means every training data set might represent a different result.

Maybe this is the fun part. In this phase, data scientists try to fit the model on a portion of the data and evaluate the model’s performance on another part of the data. The first portion is the training set. The second one is the validation set. Sometimes there is a third portion called the test set. It should be noted that sometimes the best model, depending on the business goal, is the most interpretable and simplest model, rather than the one with the highest predictive accuracy. It depends on the business goal, the practical action, and if there is any regulation in the industry.

## 统计代写|统计与机器学习作业代写Statistical and Machine Learning代考|Provide an Answer

The fifth and last step is to provide answers to the original questions, the ones raised and validated during the first step. Some pertinent questions are:

• What lessons were learned from the trained models?
• How do the trained models answer the original questions?
• How do the trained models tell a story and support business decisions?
• How can the trained models be used and deployed in production in the appropriate time frame to support the required business actions?

For example, can the trained model support a campaign that targets customers with the highest probabilities of churn and offer them incentives to keep them using and consuming the company’s products and services? Can the trained model support a fraud detection process in real time to identify possible fraudulent business transactions? The model’s results might be very accurate, but to benefit the organization, the model should be deployed in an appropriate time frame. For example, in cybersecurity, if the model does not generate real-time alerts in a way that fraud analysts can take immediate actions, then the model might be useless, since digital attacks must be identified within seconds, not weeks or months.
Once an answer is provided, it might generate more questions regarding the business problem. Therefore, the data science lifecycle is cyclical as the process is repeated until the business problem is solved.
The entire analytical process and the data science approach can be viewed as a dynamically evolving flow as shown in Figure 1.4. In data science, the more complex the analytical task, the more value added to the business. For example, a simple query report can add value to the business by simply illustrating the relationships in the data, showing what happened in the past. It is very much descriptive in the sense that nothing can be done to change that historical event. However, awareness is the first step to understand the business problem and aim for an analytical solution.

Data exploration analyses can add further value to the business with more complex queries to the data. Multi-dimensional queries can help business analysts to not only understand what happened, but why it happened in that way. Analyzing the historical data under multiple dimensions at the same time can answer many questions about the business, the market, and the scenarios. Data mining, analytics, or data science, regardless of the name, it is a further step to gain knowledge about the business. Some analytical models explain what is going on right now. Unsupervised models such as clustering, segmentation, association analysis, path analysis, and link analysis help business analysts understand what exactly happens in a very short time frame and allow companies to deploy business actions to take advantage of this knowledge. Furthermore, supervised models can learn from past events and predict and estimate future occurrences. Data science in this phase is basically trying to know what will happen in the future. This is very similar to econometric and forecast models trying to foresee what will happen soon with a business event.

# 统计与机器学习代考

## 统计代写|统计与机器学习作业代写统计和机器学习代考|对数据建模

.

• 哪个模型预测精度最高?
• 哪个模型对新数据的泛化效果最好?
• 是否可以验证模型?有可能对模型进行测试吗?有可能在新的数据上诚实地测试模型吗?
• 哪个模型最容易解释?
• 哪个模型最好地解释了输入变量和目标之间的相关性?哪一个最能描述预测因子对估计的影响?
• 哪个模型最能解决业务目标?这是数据科学家的游乐场，他们在这里使用不同的算法、技术和不同的分析方法!是的，很多建模过程只是尝试新的算法和评估结果。数据科学不同于一些精确科学，如数学和物理，后者基于一个健壮的方程和输入，可以预测输出。在数据科学中，输入集可能是已知的，但在模型训练结束之前，预测器的确切子集仍然是未知的。该方程是在模型训练过程中根据输入数据建立的。然后结果就出来了。输入数据集的任何更改都意味着输出数据集的更改。因此，数据科学与统计和数学算法密切相关。然而，其余的一切都是艺术。此外，许多模型并没有达到应有的健壮性。有些模型或算法是非常不稳定的，这意味着每个训练数据集可能代表不同的结果

也许这是有趣的部分。在这个阶段，数据科学家试图根据一部分数据拟合模型，并在另一部分数据上评估模型的性能。第一部分是训练集。第二个是验证集。有时还有第三部分叫做测试集。应该注意的是，有时最佳模型(取决于业务目标)是最可解释和最简单的模型，而不是具有最高预测精度的模型。这取决于商业目标，实际行动，以及这个行业是否有规章制度
统计代写|统计与机器学习作业代写统计和机器学习代考|提供一个答案
第五步，也是最后一步，是对最初的问题，即在第一步中提出和验证的问题，提供答案。一些相关的问题是:
• 从训练的模型中学到了什么?
• 经过训练的模型如何回答原始问题?
• 经过训练的模型如何讲故事和支持业务决策?
• 如何在适当的时间范围内使用和部署经过培训的模型，以支持所需的业务操作?

例如，训练过的模型是否能够支持一项针对流失率最高的客户的活动，并为他们提供激励，以保持他们使用和消费公司的产品和服务?训练过的模型能否实时支持欺诈检测流程以识别可能的欺诈业务交易?模型的结果可能非常准确，但是为了使组织受益，应该在适当的时间框架内部署模型。例如，在网络安全领域，如果该模型不能生成实时警报，使欺诈分析人员能够立即采取行动，那么该模型就可能毫无用处，因为数字攻击必须在几秒钟内识别出来，而不是几周或几个月。一旦提供了一个答案，它可能会产生更多关于业务问题的问题。因此，数据科学的生命周期是周期性的，因为过程是重复的，直到业务问题得到解决。整个分析过程和数据科学方法可以被视为一个动态演变的流程，如图1.4所示。在数据科学中，分析任务越复杂，为业务增加的价值就越多。例如，通过简单地说明数据中的关系，显示过去发生的事情，一个简单的查询报告可以为业务增加价值。从某种意义上说，没有什么可以改变这一历史事件，它非常具有描述性。然而，意识是理解业务问题并以分析解决方案为目标的第一步

数据探索分析可以通过对数据更复杂的查询为业务增加进一步的价值。多维度查询不仅可以帮助业务分析人员理解发生了什么，还可以帮助他们理解为什么会发生这种情况。同时在多个维度下分析历史数据可以回答许多关于业务、市场和场景的问题。数据挖掘、分析或数据科学，不管名字是什么，它都是获取业务知识的进一步步骤。一些分析模型解释了目前的情况。无监督的模型，如聚类、细分、关联分析、路径分析和链接分析，帮助业务分析人员了解在非常短的时间内到底发生了什么，并允许公司部署业务操作来利用这些知识。此外，监督模型可以从过去的事件中学习，并预测和估计未来的事件。这个阶段的数据科学基本上是试图知道未来会发生什么。这非常类似于计量经济学和预测模型，试图预测一个商业事件很快会发生什么

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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。

assignmentutor™您的专属作业导师
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