# 计算机代写|机器学习代写machine learning代考|CS446

## 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.

The examples show that there is a problem with the business model for machine learning. The companies that profit from black-box models for high-stakes decisions are not entirely responsible for the quality of individual predictions or explanations of those predictions because no one knows what is happening inside the model and how the results are generated. The best we can do is apply an explainable method and deduce the mechanism. For this reason, interpretable ML is not very popular and is not encouraged. The argument for favoring the black box instead of interpretable ML is that the black box prevents reverse engineering.

## 计算机代写|机器学习代写machine learning代考|The Key Differences Between Two Choices

The ability to explain a phenomenon at a conceptual level is very different from generating predictions at a measurable level. This disparity is created by the operationalization of theories into statistical models and measurable data.
To convey this difference properly, consider a theory that $\mathrm{X}$ causes $\mathrm{Y}$, and let’s describe it using the function $F$, such that $Y=F(X)$.

F can be considered a model with two dimensions of inputs and outputs, where $\mathrm{X}$ is the input construct and $\mathrm{Y}$ is the output. F can be any model with the behavior of optimization or prediction.

Because $\mathrm{F}$ is usually not sufficiently detailed to lead to a single $\mathrm{f}$, a set of f-models is often considered.

In explanatory modeling, the objective is to match $\mathrm{f}$ and $\mathrm{F}$ as closely as possible for the statistical inference and prove theoretical hypotheses. The $\mathrm{X}$ and $\mathrm{Y}$ data are tools for estimating $\mathrm{f}$, which tests the causal hypotheses. The objective of explanatory modeling is to understand the relationship between $\mathrm{X}$ and $\mathrm{Y}$ and how changes in $\mathrm{X}$ govern changes in Y. Another objective is to understand how the f mechanisms work while taking input of $X$ and producing an output of $Y$.
In contrast, in predictive modeling, the X, Y, and f entities are combined to create good predictions of new $\mathrm{Y}$ values. Even if the underlying causal relationship is $\mathrm{Y}=\mathrm{F}(\mathrm{X})$, a function other than $f(X)$ and data other than $X$ might be preferable for prediction.
Four primary aspects can explain the differences.

# 机器学习代考

## 计算机代写|机器学习代写machine learning代考|The Key Differences Between Two Choices

F 可以被认为是具有输入和输出两个维度的模型，其中X是输入构造并且是是输出。F 可以是任何具有优化或预测行为的模型。

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