全部

What are the best ways to explain dimensionality reduction to non-technical stakeholders?

由人工智能和领英社区提供技术支持

Dimensionality reduction is a data analytics technique that simplifies complex data sets by reducing the number of features or variables. It can help improve the performance, accuracy, and interpretability of machine learning models, as well as reduce the computational cost and storage space. However, explaining dimensionality reduction to non-technical stakeholders can be challenging, especially if they are not familiar with the concepts of data, features, and models. In this article, you will learn some of the best ways to explain dimensionality reduction to non-technical stakeholders, using simple examples, analogies, and visualizations.

本文章的要点总结

Use analogies:

Explain dimensionality reduction like sharing the gist of a complex story. You distill a novel down to key points, keeping the essence for easier understanding and better decision-making.
Principal component analysis:

This method ranks data features by their impact and keeps only the most influential ones. It's like focusing on the chapters of a book that drive the story forward, trimming the rest.

本摘要由 AI 和以下专家提供支持

1 What is dimensionality reduction?

The first step is to explain what dimensionality reduction is and why it is useful. You can start by introducing the idea of data as a collection of information about different aspects or features of something. For example, if you have data about customers, you might have features such as age, gender, location, income, preferences, etc. Each feature adds a dimension to the data, making it more complex and harder to analyze. Dimensionality reduction is a way of reducing the number of features or dimensions, while keeping the most relevant and important information. This can help you find patterns, insights, and relationships in the data more easily and efficiently.

添加您的观点

Sami Al-Mohaimeed, PMP

Senior Licensing Consultation Specialist @ Ministry of Investment | PMP, SAP
举报内容
Explain dimensionality reduction as a technique simplifying complex data by retaining essential information, making it easier to understand. It's akin to summarizing a book's main ideas into a few key points for clearer comprehension and strategic decision-making.

已翻译

赞
Yolande Athaide, MBA, JD

Responsible AI | Data-Informed Insights | Business Leadership & Strategy
举报内容
Using dimensionality reduction also reduces an overfit model, as adding too many features makes the model very tailored to the training data. And if some of the features are correlated with each other, that can also cause overfitting.

已翻译

赞

2 How does dimensionality reduction work?

The next step is to explain how dimensionality reduction works and what are some of the common methods. You can use simple examples and analogies to illustrate the process and the logic behind it. For example, you can compare dimensionality reduction to summarizing a long text or compressing an image. You can also explain that there are two main types of dimensionality reduction: feature selection and feature extraction. Feature selection is when you choose a subset of features that are most relevant and discard the rest. Feature extraction is when you create new features that are combinations of the original ones, capturing the most variation and information in the data.

添加您的观点

Yolande Athaide, MBA, JD

Responsible AI | Data-Informed Insights | Business Leadership & Strategy
举报内容
One method for reducing the number of features (which reduces the dimension of the matrix of data - rows of observations times columns of features) is principal component analysis. You order the features in decreasing order of their impact on the target variable, and retain only those top 3-5 that cause, say, 80% of the impact on the target for the analysis.

已翻译

赞

3 What are the benefits of dimensionality reduction?

The third step is to explain the benefits of dimensionality reduction and how it can help achieve the goals and objectives of the project. Dimensionality reduction can improve the performance and accuracy of machine learning models by removing noise, redundancy, and irrelevant features. It also reduces computational cost and storage space by using less data and resources. Additionally, dimensionality reduction enhances the interpretability and explainability of machine learning models by simplifying the data and making it easier to understand and communicate. Furthermore, it facilitates the visualization and exploration of data by reducing the number of dimensions and enabling the use of plots and charts.

添加您的观点

Yolande Athaide, MBA, JD

Responsible AI | Data-Informed Insights | Business Leadership & Strategy
举报内容
Benefits of dimensionality reduction: - stronger (less overfit to the training data) model with better performance over unseen data - reduction of redundant features and noise in the system - more cost effective for computation and storage, fewer resources - improved interpretability and explainability since the data becomes easier to understand and discuss - easier analysis, exploration, and visualization of data with an easily manageable set of features

已翻译

赞

4 What are the challenges of dimensionality reduction?

Explaining the challenges of dimensionality reduction and how to overcome them is the fourth step. You should be aware of the limitations and drawbacks, such as losing information and detail by reducing the number of features or dimensions. Additionally, you must choose the right method and parameters based on the data and problem, as well as evaluate the quality and effectiveness of dimensionality reduction with metrics and criteria. It's also important to consider the trade-off between simplicity and complexity, as well as between accuracy and interpretability.

添加您的观点

Yolande Athaide, MBA, JD

Responsible AI | Data-Informed Insights | Business Leadership & Strategy
举报内容
Challenges and drawbacks include: - potential loss of key information if a major feature is accidentally left out - choice of best reduction method and parameters for a given dataset and issue under study - appropriate metrics to evaluate effectiveness of the chosen method - tradeoff between simpler versus more complex models; and between accuracy or sensitivity of the model and interpretability

已翻译

赞

5 What are some examples of dimensionality reduction?

Explaining examples of dimensionality reduction and how they are applied in different domains and scenarios is the fifth step. You can use real-world cases and stories to illustrate the benefits of dimensionality reduction. For instance, it can be used in image recognition and compression to reduce the size and complexity of images while still preserving essential features and information. Additionally, it can be used in text analysis and summarization to extract the main topics and themes from large and diverse texts while decreasing the noise and redundancy. Lastly, it can be used in customer segmentation and personalization to identify the key characteristics and preferences of different customer groups, while making the data and models easier to understand.

添加您的观点

6 How to communicate dimensionality reduction to non-technical stakeholders?

The sixth and final step is to explain how to communicate dimensionality reduction to non-technical stakeholders and how to address their questions and concerns. To make your explanation clear, concise, and convincing, use simple and familiar language, avoiding jargon and technical terms. You can also use examples, analogies, visualizations, stories, narratives, data, and evidence to demonstrate how dimensionality reduction can help solve problems and create value. Invite questions and comments from the stakeholders and listen to their needs and expectations.

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

Data Analytics

+ 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

What are the best ways to explain dimensionality reduction to non-technical stakeholders?

1

2

3

4

5

6

7

1 What is dimensionality reduction?

2 How does dimensionality reduction work?

3 What are the benefits of dimensionality reduction?

4 What are the challenges of dimensionality reduction?

5 What are some examples of dimensionality reduction?

6 How to communicate dimensionality reduction to non-technical stakeholders?

7 Here’s what else to consider

Data Analytics

给文章评分

感谢您的反馈

更多Data Analytics相关文章

更多相关阅读内容