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CS代写|机器学习代写Machine Learning代考|Performance Measure

In order to evaluate the generalization ability of models, we need not only practical and effective estimation methods but also some performance measures that can quantify the generalization ability. Different performance measures reflect the varied demands of tasks and produce different evaluation results. In other words, the quality of a model is a relative concept that depends on the algorithm and data as well as the task requirement.

In prediction problems, we are given a data set $D=$ $\left{\left(\boldsymbol{x}1, y_1\right),\left(\boldsymbol{x}_2, y_2\right), \ldots,\left(\boldsymbol{x}_m, y_m\right)\right}$, where $y_i$ is the ground-truth label of the sample $\boldsymbol{x}_i$. To evaluate the performance of a learner $f$, we compare its prediction $f(\boldsymbol{x})$ to the ground-truth label $y$. For regression problems, the most commonly used performance measure is the Mean Squared Error (MSE): $$E(f ; D)=\frac{1}{m} \sum{i=1}^m\left(f\left(\boldsymbol{x}i\right)-y_i\right)^2 .$$ More generally, for a data distribution $\mathcal{D}$ and a probability density function $p(\cdot)$, the MSE is written as $$E(f ; \mathcal{D})=\int{\boldsymbol{x} \sim D}(f(\boldsymbol{x})-y)^2 p(\boldsymbol{x}) d \boldsymbol{x} .$$
The rest of this section will introduce some common performance measures for classification problems.

CS代写|机器学习代写Machine Learning代考|Error Rate and Accuracy

At the beginning of this chapter, we discussed error rate and accuracy, which are the most commonly used performance measures in classification problems, including both binary classification and multiclass classification. Error rate is the proportion of misclassified samples to all samples, whereas accuracy is the proportion of correctly classified samples instead. Given a data set $D$, we define error rate as
$$E(f ; D)=\frac{1}{m} \sum_{i=1}^m \mathbb{I}\left(f\left(\boldsymbol{x}i\right) \neq y_i\right)$$ and accuracy as \begin{aligned} \operatorname{acc}(f ; D) &=\frac{1}{m} \sum{i=1}^m \mathbb{I}\left(f\left(\boldsymbol{x}i\right)=y_i\right) \ &=1-E(f ; D) . \end{aligned} More generally, for a data distribution $\mathcal{D}$ and a probability density function $p(\cdot)$, error rate and accuracy can be, respectively, written as \begin{aligned} E(f ; \mathcal{D}) &=\int{\boldsymbol{x} \sim D} \mathbb{I}(f(\boldsymbol{x}) \neq y) p(\boldsymbol{x}) d \boldsymbol{x}, \ \operatorname{acc}(f ; \mathcal{D}) &=\int_{\boldsymbol{x} \sim D} \mathbb{I}(f(\boldsymbol{x})=y) p(\boldsymbol{x}) d \boldsymbol{x} \ &=1-E(f ; \mathcal{D}) . \end{aligned}

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。

Posted on Categories:Machine Learning, 机器学习, 计算机代写

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计算机代写|机器学习代写Machine Learning代考|Communicating and Summarizing Model Behavior

To identify and integrate human expectations of interpretability, it is very important to understand and identify the edge cases. This edge case identification can happen by a group of people using the model or by an individual

The following are some of the benefits of integrating interpretability in a modelbuilding exercise.

It helps humans articulate the expectations they have with predictions in interpretability tasks.

It helps humans effectively recognize gaps between what they expect from a system and the actual representations.

It helps humans debug/change model behavior based on their findings.

Data scientists often engage in such collaborative work. Having access to stakeholders’ responses to other different types of models is an important component of model development and building stage.

One beneficial approach can be developing interactive applications that stakeholders or end users can use to efficiently provide feedback on the model usage. Researchers have developed platforms to find errors made by predictive models to gather feedback from a group of people. Interactive applications and approaches can enable domain experts and ML experts to engage with models and share knowledge about decision processes.

计算机代写|机器学习代写Machine Learning代考|Interpretability Is Cooperative

Since the models are cooperative and social in the building phase, a lot of knowledge transfer happens in model hand-offs. To identify the model bugs that occur after deployment, it is very beneficial to have visualizations that can capture the relationship between the model and those receiving the models. In short, a good summary of model behavior provides a great deal of relief to stakeholders and end users to understand the working of the model and enable developers to solve model bugs quickly.

These can be developed along with the algorithms which identify the edge cases.
One important concern is to improve approaches where the output does not agree with human behavior or perception.

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。

Posted on Categories:Machine Learning, 机器学习, 计算机代写

avatest™帮您通过考试

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计算机代写|机器学习代写Machine Learning代考|Human-Friendly Themes Characterizing Interpretability Work

Research on interpretability to produce new tools or insights has primarily focused on the interaction between an individual and a model. Interpretability is described as a property of the model class based on its relation to human expectations or constraints, such as monotonicity or additivity, or human-driven constraints that come from domain knowledge or the presentation of a specific model’s results that enables a human to understand why a label was applied to an instance (e.g., LIME or SHAP).
We summarize four themes that are different from the technical characterization we mentioned in the previous chapters. These characterizations are more human-centric and talk about how humans can work together with interpretability to make models more useful.

计算机代写|机器学习代写Machine Learning代考|Interpretability Is Cooperative

Interpretability in an organization means collaboration and coordinating of values and knowledge between stakeholder roles. This theme is defined majorly by mentions of discussion with other stakeholder groups, such as domain experts. These discussions frequently occur during ideation and model building, and validation, but also after deployment. Collaboration around interpretability is important for improving business reasoning and convincing stakeholders that the models that have been built will bring value.

Interpretability’s role in building and maintaining trust between people in an organization is another clear way its social nature was evident.

People respond to use cases and anecdotes way better than they respond to math. Hence, the proof and the need for interpretability should be adopted when communicating to a client. It’s usually to explain the operation of the system and then give some answer.
When public relations, tech transfer, or other teams further from model development gained trust through interpretability, limitations or constraints on models tended to be more easily accepted with a model.

The internal trust frees up teams from constraints on the types of models they could use or the level of monitoring required, such that model interpretability was seen as a means to obtaining a certain status.

Sometimes interpretability’s importance was not that it could explain any decision, but that the act of including it alone signaled a “due diligence” that other stakeholders or end users found comforting.

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。

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计算机代写|机器学习代写Machine Learning代考|Deployment, Maintenance, and Use Stage

Once the technical experts finalize the model after several reviews and enhancements, deploy the models by integrating them into the actual data feed. In the integration phase, the interpretability issues are not limited to understanding the model behavior but also extend to the whole infrastructure around the model.

There might be a bit of confusion around the goal of interpretability as to whether it is for understanding how the model works or how a whole model pipeline works. However, the ecosystem in which models exist is as important as the model itself. In these scenarios, interpretability issues may arise due to the black-box nature of the model and the complex pipelines that are part of the software. The complexity in the flow of information within a model framework can also cause interpretability issues.
In the life cycle of an ML system, interpretability challenges arise after a model has been put in production. Some of the relevant problems are deciphering how to check instances/prediction patterns that do not reflect the state-of-the-art behavior and checking why mode has made an unacceptable error while supporting high-stakes decisions. Such problems are detected by the team which is responsible for monitoring the model. Once the error is identified, it is classified whether we need an interpretability method to determine the cause of the error or not. This type of “root cause analysis” can be particularly challenging.

计算机代写|机器学习代写Machine Learning代考|Interpretability for Model Validation and Improvement

One of the most important activities during model development is identifying issues with the model and finding solutions to fix them. The model technical experts and reviewers should fulfill this goal during the model development journey. However, they also sometimes need inputs to effectively achieve this goal. For example, auditors and legal teams may help experts and reviewers to identify a stringent set of legal requirements while fixing any issues. One of the ways to validate models is to check up on aggregate statistics of the model’s outputs; however, we need interpretability tools and methods on top of it.

Sometimes models can learn non-essential and non-meaningful relationships between input and output with high accuracy. This might make the involvement of domain experts necessary for the proper interpretation of results. Identifying irrelevant correlations and false implications of causation may require the involvement of experts who understand the model and its mechanisms. This process is characterized by contrasting model behavior with the stakeholders’ perceptions of the actual meaning of the data and model predictions.
From a legal standpoint, transparency is needed to make sure models are not violating legal regulations such as the Fair Trading Act or General Data Protection Regulation, which aim to prevent discriminatory practices. When a model makes a mistake, it is crucial to explain why a given decision was made. Compliance is one of the main reasons why interpretability is needed.

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。

Posted on Categories:Machine Learning, 机器学习, 计算机代写

计算机代写|机器学习代考MACHINE LEARNING代考|LSML22 Lucidness

avatest.org™机器学习Machine Learning代写，免费提交作业要求， 满意后付款，成绩80\%以下全额退款，安全省心无顾虑。专业硕 博写手团队，所有订单可靠准时，保证 100% 原创。avatest.org™， 最高质量的机器学习Machine Learning作业代写，服务覆盖北美、欧洲、澳洲等 国家。 在代写价格方面，考虑到同学们的经济条件，在保障代写质量的前提下，我们为客户提供最合理的价格。 由于统计Statistics作业种类很多，同时其中的大部分作业在字数上都没有具体要求，因此机器学习Machine Learning作业代写的价格不固定。通常在经济学专家查看完作业要求之后会给出报价。作业难度和截止日期对价格也有很大的影响。

avatest.org™ 为您的留学生涯保驾护航 在澳洲代写方面已经树立了自己的口碑, 保证靠谱, 高质且原创的澳洲代写服务。我们的专家在机器学习Machine Learning代写方面经验极为丰富，各种机器学习Machine Learning相关的作业也就用不着 说。

计算机代写|机器学习代考MACHINE LEARNING代考|Lucidness

When we must judge how well humans understand explanations, we look at the lucidness property of the methods. Lucidness is most difficult to define and measure.

Interpretability is a subjective concept, where properties depend on the audience and the context in which the methods are used. It is a measure of how comprehensible the features are. Several modules within a method might make an explanation less understandable; for example, the transformation of features.

计算机代写|机器学习代考MACHINE LEARNING代考|Reliability

Reliability is linked to the certainty of the ML model. Many ML models only provide prediction values and do not contain any statement on the model’s confidence in the correctness of the prediction. The reliability property of explanations for interpretability helps instill confidence in the users of the model regarding the correctness of the model.

计算机代写|机器学习代考MACHINE LEARNING代考|Significance

Significance measures how well the explanations reflect the importance of the features of their own parts. For example, if the explanations generate a decision rule list as an explanation for the model significance, which of the rule’s conditions was the most important?

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。

Posted on Categories:Machine Learning, 机器学习, 计算机代写

计算机代写|机器学习代考MACHINE LEARNING代考|CS7641 Loyalty

avatest.org™机器学习Machine Learning代写，免费提交作业要求， 满意后付款，成绩80\%以下全额退款，安全省心无顾虑。专业硕 博写手团队，所有订单可靠准时，保证 100% 原创。avatest.org™， 最高质量的机器学习Machine Learning作业代写，服务覆盖北美、欧洲、澳洲等 国家。 在代写价格方面，考虑到同学们的经济条件，在保障代写质量的前提下，我们为客户提供最合理的价格。 由于统计Statistics作业种类很多，同时其中的大部分作业在字数上都没有具体要求，因此机器学习Machine Learning作业代写的价格不固定。通常在经济学专家查看完作业要求之后会给出报价。作业难度和截止日期对价格也有很大的影响。

avatest.org™ 为您的留学生涯保驾护航 在澳洲代写方面已经树立了自己的口碑, 保证靠谱, 高质且原创的澳洲代写服务。我们的专家在机器学习Machine Learning代写方面经验极为丰富，各种机器学习Machine Learning相关的作业也就用不着 说。

计算机代写|机器学习代考MACHINE LEARNING代考|Loyalty

Loyalty is one of the essential properties of an explanation because low loyalty is essentially useless. By loyalty, we mean how well the explanations approximate the model predictions. High accuracy and high loyalty go hand in hand. However, some methods only provide local loyalty, which means they only approximate well to a single instance or group.

计算机代写|机器学习代考MACHINE LEARNING代考|Dependability

Dependability is measured in the context of two different models trained on the same task and with similar output predictions. This property is related to how different the explanations are between them.
If the explanations are very similar, the explanations are highly dependent. However, this property is tricky since the two models could use different features but get similar predictions. In this specific case, high dependability is not desirable because the explanations should be very different, as the models use different relationships for their predictions. This property is desirable when the models rely on similar relationships; otherwise, explanations reflect different aspects of the data that the models rely on.

计算机代写|机器学习代考MACHINE LEARNING代考|Resoluteness

Resoluteness represents the similarity of the explanations of similar instances. Stability compares the explanations between similar instances for a fixed model instead of dependability, which compares explanations between different models.
High stability represents the fact that small variations in the values of the model’s features do not result in substantial changes in the explanations unless there is a great change in the prediction itself. Specific components of an explanation method like sampling methods can also be attributed to the lack of stability. However, stability is always desirable.

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。

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计算机代写|机器学习代考MACHINE LEARNING代考|CSE446 Algorithmic Feasibility

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计算机代写|机器学习代考MACHINE LEARNING代考|Algorithmic Feasibility

Algorithmic feasibility is related to the computational complexity of the method. Although some methods promise to give meaningful results, they are so complex that it takes a lot of time and resources to run them and get explanations. Computation time is of the main drawbacks of some of the very well-known methods. Apart from the time, some algorithmic properties need to be considered to define feasibility for the methods. Some methods include a data sampling process which means that the explanations might not be stable. This means that repeating the same instance and model with the same parameters can result in a different explanation.

Now that we have discussed the properties of the explanation methods, let’s look at the explanations generated by these methods.

计算机代写|机器学习代考MACHINE LEARNING代考|Properties of Individual Explanations

Certain properties apply to explanations rather than the whole method. Next, let’s discuss the properties which are critical for evaluating the explanations generated by the methods. An explanation usually relates the feature values of an instance to its model prediction in a humanly understandable way.

计算机代写|机器学习代考MACHINE LEARNING代考|Correctness

Correctness property is related to the accuracy of the explanation regarding the test data or unseen data. One concession we get when we evaluate an interpretability method is that if the accuracy of the black-box model is in itself, then you can accept lower accuracy in the explanations.

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。