What is the best feature selection technique for your dataset?

由人工智能和领英社区提供技术支持

Feature selection is the process of choosing a subset of relevant features from a large and complex dataset, with the aim of improving the performance, interpretability, and efficiency of machine learning models. However, there is no one-size-fits-all solution for feature selection, as different techniques may suit different types of data, objectives, and assumptions. In this article, you will learn about some of the most common and useful feature selection techniques, and how to choose the best one for your dataset.

在这篇协作文章中查找专家回答

由社区从 4 条内容中精选。了解更多

1 Filter methods

Filter methods are based on the intrinsic properties of the features, such as their correlation, variance, or information gain. They do not involve any learning algorithm, and are therefore fast and independent of the model. However, they may ignore the interactions between the features and the target variable, and may not capture the optimal subset for a specific task. Filter methods are suitable for exploratory analysis, preliminary screening, and large datasets.

添加您的观点

Mohsen Falahat

???Data Engineer by Day, Data Scientist by Night From Pipelines to Predictions Follow for Latest AI News
举报内容
Filter methods in feature selection are quick and model-independent. They analyze feature properties like correlation, variance, and information gain. They are great for initial data exploration, preliminary screening, and large datasets. However, they don't consider feature interactions and may not yield the best feature subset for a specific task. Use them when you need a fast, initial feature selection, but be aware of their limitations.

已翻译

赞
Chukwuemeka Eborah

Microbiologist | Data Entry Expert | Life Style | Data Analyst
举报内容
Filter methods Which select features from a dataset independently for any machine learning algorithm. These methods rely only on the characteristics of these variables, so features are filtered out of the data before learning begins.

已翻译

赞

2 Wrapper methods

Wrapper methods are based on the performance of a learning algorithm on different subsets of features. They use a search strategy, such as forward, backward, or recursive elimination, to select the subset that maximizes a predefined criterion, such as accuracy, F1-score, or AUC. They are more computationally intensive and prone to overfitting than filter methods, but they can capture the interactions between the features and the target variable, and tailor the subset to a specific task. Wrapper methods are suitable for small to medium datasets, and when the model is fixed.

添加您的观点

Mohsen Falahat

???Data Engineer by Day, Data Scientist by Night From Pipelines to Predictions Follow for Latest AI News
举报内容
An example of when to use wrapper methods is in a scenario where you want to fine-tune the feature subset for a specific machine learning model. For instance, if you're working on a medical diagnosis task using a small dataset and you've preselected a support vector machine (SVM) as your model, wrapper methods, like recursive feature elimination, can be applied to iteratively select the most relevant features for that SVM model. This approach helps optimize the model's performance by considering interactions between features, which could be critical in healthcare applications.

已翻译

赞

3 Embedded methods

Embedded methods are based on the incorporation of feature selection within the learning algorithm. They use regularization, such as L1 or L2 norm, or tree-based methods, such as random forest or gradient boosting, to penalize or rank the features according to their importance. They are more efficient and robust than wrapper methods, but they may not be as flexible or interpretable. Embedded methods are suitable for medium to large datasets, and when the model is compatible.

添加您的观点

Mohsen Falahat

???Data Engineer by Day, Data Scientist by Night From Pipelines to Predictions Follow for Latest AI News
举报内容
Embedded methods are best for medium to large datasets when you have flexibility in choosing your model. They efficiently incorporate feature selection within the learning algorithm, making them computationally robust. However, they may sacrifice some interpretability due to techniques like regularization or tree-based methods. Use them when efficiency is a priority and you're okay with some loss in interpretability.

已翻译

赞

4 Hybrid methods

Hybrid methods are based on the combination of filter and wrapper methods, or filter and embedded methods, to leverage the advantages of both approaches. They use filter methods to reduce the dimensionality and noise of the data, and then use wrapper or embedded methods to fine-tune the subset according to the model performance. They are more effective and reliable than filter methods alone, but they may still be costly and complex. Hybrid methods are suitable for complex and heterogeneous datasets, and when the model is not fixed.

添加您的观点

5 How to choose

Choosing the best feature selection technique for your dataset is dependent on several factors, such as the size, quality, and structure of the data, the goal and complexity of the task, and the type and parameters of the model. It’s suggested to start with filter methods to get a quick overview of the features and their relationships. Wrapper methods are appropriate for small datasets with a fixed model. Embedded methods are suitable for medium to large datasets with a compatible model. Hybrid methods are ideal for complex and heterogeneous datasets with a flexible model. Experimenting with different techniques and evaluating trade-offs between the number of features, model performance, and interpretability of the subset is recommended. Ultimately, select the technique that best suits your data, objective, and assumptions that provides a meaningful and robust subset for your model.

添加您的观点

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

Data Mining

+ 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

What is the best feature selection technique for your dataset?

1

2

3

4

5

6

1 Filter methods

2 Wrapper methods

3 Embedded methods

4 Hybrid methods

5 How to choose

6 Here’s what else to consider

Data Mining

给文章评分

感谢您的反馈

更多Data Mining相关文章

更多相关阅读内容

What is the best feature selection technique for your dataset?

1

2

3

4

5

6

1 Filter methods

2 Wrapper methods

3 Embedded methods

4 Hybrid methods

5 How to choose

6 Here’s what else to consider

Data Mining

给文章评分

感谢您的反馈

查看其他技能