How do you refine your feature engineering strategies?

由人工智能和领英社区提供技术支持

Feature engineering is the process of creating, selecting, and transforming features from raw data to improve the performance of machine learning models. It is a crucial step in exploratory data analysis, as it can reveal hidden patterns, reduce noise, and increase interpretability. However, feature engineering is not a one-time task. You must update and refine your strategies based on new data. Here are some tips on how to do that.

此文章中的业界达人

由社区从 2 条内容中精选。了解更多

1 Get feedback from stakeholders

The sources of feedback for your feature engineering strategies include the stakeholders of your project, like clients, managers, or domain experts. They can provide valuable insights into the business problem, data quality, and expectations of the model. Communicate with them regularly and present your results and assumptions. They can help you validate your hypotheses, suggest new features, and identify potential errors or biases in your data.

添加您的观点

2 Evaluate your model performance

Use appropriate metrics and methods to measure how well your model fits the data and generalizes to new data. Also compare different models and feature sets to see which ones perform better. You can use techniques such as cross-validation, grid search, or feature importance to assess the impact of your features on your model. Also monitor your model performance over time and check for any changes or anomalies.

添加您的观点

Tashi Tamang

Data Analyst @ WALMART |SQL & PYTHON Specialist | Power BI, Tableau | ML, AWS, Azure||
举报内容
Refining feature engineering strategies starts with evaluating model performance to identify areas for improvement. By iteratively testing and validating features, you can uncover the most impactful variables. This approach ensures your model is both accurate and robust.

已翻译

赞

3 Explore new data sources

Explore new data sources that can enrich or complement your existing data. You can look for external data sources that are relevant to your problem domain, such as public datasets, APIs, or web scraping. You can also collect new data from your own sources, like surveys, experiments, or sensors. Then integrate the new data with your existing data and apply the same feature engineering steps. And don't forget to check for any inconsistencies, conflicts, or redundancies in your data.

添加您的观点

4 Experiment with different techniques

Try different methods of data cleaning, imputation, encoding, aggregation, or dimensionality reduction. Also use libraries or frameworks that can automate or simplify some feature engineering tasks, including scikit-learn, pandas, or featuretools. Always test and compare the results of different techniques and tools and choose the ones that suit your data best.

添加您的观点

5 Learn from others

Read articles, blogs, books, or papers that showcase different feature engineering approaches. Also consider joining online communities, forums, or courses that discuss feature engineering topics. Or you can participate in competitions, hackathons, and projects that can expose you to new data and problems and give you feedback for your feature engineering strategies.

添加您的观点

Dr. Uohna Thiessen

Leading AI Strategist, empowering businesses to leverage AI automation (chatbots, conversational AI, & AI agents) to drive growth and innovation.
(已编辑)
举报内容
Seasoned data professionals understand that Exploratory Data Analysis (EDA) is essential to successful Feature Engineering (FE). Comprehensive EDA allows for a quicker feature engineering process, making it easier to design data transformations that lead to the optimization of your models' performance. If you want to streamline your feature engineering process, be sure to perform EDA on your data, it can be a game-changer. Gaining a deeper understanding of your data makes identifying key patterns and insights that inform your feature selection and this speed up the entire FE process. Don't underestimate the power of EDA!

已翻译

赞

6 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

Exploratory Data Analysis

+ 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 do you refine your feature engineering strategies?

1

2

3

4

5

6

1 Get feedback from stakeholders

2 Evaluate your model performance

3 Explore new data sources

4 Experiment with different techniques

5 Learn from others

6 Here’s what else to consider

Exploratory Data Analysis

给文章评分

感谢您的反馈

更多Exploratory Data Analysis相关文章

更多相关阅读内容