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# 计算机代写|机器学习代写Machine Learning代考|KIT315 Key Issues with Interpretable ML

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## 计算机代写|机器学习代写Machine Learning代考|Key Issues with Interpretable ML

There are many cases where black boxes with explanations are preferred over interpretable models, even for high-stakes decisions. Interpretable models might sometimes have computational problems or problems with the training of researchers and the availability of code. The following sections highlight some of the major issues with interpretable ML.

## 计算机代写|机器学习代写Machine Learning代考|Profits vs. Losses

Companies can make profits from the trademarks of the prediction generated by their black-box models, but interpretable ML can result in losses. Interpretable ML can result in losses for companies who try to earn money through good-performing in-house-built black-box algorithms. Interpretable ML does not require explainable methods. It is easy to understand the mechanism behind the model. Thus, it destroys the use case for using complex high accuracy black box models.
A recidivism tool $^1$ for risk prediction is widely used in the US judiciary system for checking or predicting which convicts can be arrested again after their release. The model’s output is simple in terms of if-then-else rules, which bases age and the number of past crimes to predict the likelihood of a person committing another crime that leads to jail. A simple interpretable ML model might be as accurate as this. However, the company behind this model has made this a proprietary software sold to the government. This model is equally accurate for recidivism prediction as to the simple three rule interpretable machine learning model involving only age and number of past crimes. However, it was sold as proprietary software to the judicial system.

In medicine, there is a trend toward blind acceptance of black-box models, which opens the door for companies to sell more models to hospitals.

## 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。