登录查看更多内容

How can you create transparent statistical models for Operations Research stakeholders?

由人工智能和领英社区提供技术支持

Operations Research (OR) is a discipline that applies mathematical and statistical methods to optimize complex systems and processes. OR practitioners often use statistical models to analyze data, identify patterns, and support decision making. However, creating transparent statistical models for OR stakeholders is not always easy. In this article, you will learn some tips and best practices to make your models more understandable, explainable, and trustworthy.

此文章中的业界达人

由社区从 3 条内容中精选。了解更多

Anubhav Srivastava

Data & AI Leader | Angel Investor | Author | 40 Under 40 Data Science | Top 10 Data Scientists (India) 2020

1 Why transparency matters

Transparency is the degree to which a statistical model can be inspected, understood, and verified by its users and stakeholders. Transparency is important for several reasons. First, it can increase the confidence and acceptance of your model results and recommendations. Second, it can help you communicate your assumptions, limitations, and uncertainties clearly and effectively. Third, it can enable you to identify and address potential biases, errors, and ethical issues in your model.

添加您的观点

Anubhav Srivastava

Data & AI Leader | Angel Investor | Author | 40 Under 40 Data Science | Top 10 Data Scientists (India) 2020
举报内容
Transparency in statistical models not only builds confidence, aids communication, and identifies biases but also ensures accountability, promotes collaboration, enhances public engagement, and facilitates educational endeavours. These aspects collectively highlight the far-reaching impact and necessity of transparency in the realm of statistical modelling. Also, in machine learning and artificial intelligence, transparency is vital for building trust between users and intelligent systems. When users can understand how a model works and why it makes certain predictions or decisions, they are more likely to trust and accept the technology.

已翻译

赞

2 How to choose transparent models

One of the first steps to create transparent statistical models is to choose the appropriate model type and complexity for your problem. There are many types of statistical models, such as linear regression, logistic regression, decision trees, neural networks, and so on. Each model has its own advantages and disadvantages, and some are more transparent than others. For example, linear regression and decision trees are relatively easy to interpret and explain, while neural networks and deep learning are often considered black boxes. You should consider the trade-off between accuracy and transparency, and select the model that best suits your data, objectives, and stakeholders.

添加您的观点

Anubhav Srivastava

Data & AI Leader | Angel Investor | Author | 40 Under 40 Data Science | Top 10 Data Scientists (India) 2020
举报内容
It is crucial to understand the nuances of data, ensuring the chosen model aligns with its characteristics, such as non-linear relationships. Rigorous cross-validation and testing on separate datasets are essential to validate a model's transparency across different scenarios. Thoughtful feature selection and the application of regularisation techniques simplify models, making them more interpretable. Incorporating domain knowledge enhances transparency, with experts guiding variable selection and model interpretation. Sensitivity analysis illuminates the model's behaviour, while thorough documentation of the entire process aids transparency by allowing others to validate the methodology.

已翻译

赞

3 How to document your models

Another key step to create transparent statistical models is to document your models thoroughly and systematically. Documentation is the process of recording and explaining the details of your model, such as the data sources, variables, parameters, assumptions, methods, results, and validation. Documentation can help you keep track of your model development, share your model with others, and justify your model choices and outcomes. You should use clear and consistent language, formats, and standards to document your models, and provide relevant references and citations.

添加您的观点

4 How to visualize your models

A powerful way to create transparent statistical models is to visualize your models and their outputs. Visualization is the use of graphs, charts, maps, and other visual elements to represent and communicate your model information. Visualization can help you explore your data, discover insights, and present your findings in an engaging and intuitive way. You should use appropriate and effective visualization tools and techniques to display your models, and follow the principles of good design and storytelling.

添加您的观点

Anubhav Srivastava

Data & AI Leader | Angel Investor | Author | 40 Under 40 Data Science | Top 10 Data Scientists (India) 2020
举报内容
Utilising visual representations such as heat-maps, scatter plots, and line graphs can elucidate relationships between variables, aiding in the interpretation of the model. Additionally, tools like partial dependence plots provide insights into how specific variables influence predictions, enhancing transparency. Utilising interactive visualisations allows stakeholders to explore model outcomes dynamically for better understanding. An underrated method is utilising colour coding and annotations in visualisations that can further highlight crucial points, making the model's behaviour more transparent and understandable.

已翻译

赞

5 How to test your models

A crucial step to create transparent statistical models is to test your models rigorously and objectively. Testing is the process of evaluating and verifying the performance, quality, and reliability of your model. Testing can help you assess how well your model fits your data, how accurate and robust your model predictions are, and how sensitive your model is to changes in inputs or assumptions. You should use various testing methods and metrics to measure your model results, and compare your model with alternative or benchmark models.

添加您的观点

6 How to explain your models

The final step to create transparent statistical models is to explain your models clearly and convincingly to your stakeholders. Explanation is the process of providing the rationale, logic, and evidence behind your model and its outputs. Explanation can help you demonstrate the value and relevance of your model, and persuade your stakeholders to accept and act on your model recommendations. You should use simple and concise language, avoid jargon and technical terms, and address the questions and concerns of your stakeholders.

添加您的观点

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

添加您的观点

Operations Research

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

查看全部

How can you create transparent statistical models for Operations Research stakeholders?

1

2

3

4

5

6

7

1 Why transparency matters

2 How to choose transparent models

3 How to document your models

4 How to visualize your models

5 How to test your models

6 How to explain your models

7 Here’s what else to consider

Operations Research

给文章评分

感谢您的反馈

更多Operations Research相关文章

更多相关阅读内容

How can you create transparent statistical models for Operations Research stakeholders?

1

2

3

4

5

6

7

1 Why transparency matters

2 How to choose transparent models

3 How to document your models

4 How to visualize your models

5 How to test your models

6 How to explain your models

7 Here’s what else to consider

Operations Research

给文章评分

感谢您的反馈

查看其他技能