登录查看更多内容

What are the top techniques for selecting features in machine learning algorithms?

由人工智能和领英社区提供技术支持

Feature selection is a crucial step in machine learning, as it can improve the performance, efficiency, and interpretability of your algorithms. However, choosing the right features from a large and complex dataset is not an easy task. In this article, you will learn about some of the top techniques for selecting features in machine learning algorithms, and how they can help you achieve your goals.

此文章中的业界达人

由社区从 5 条内容中精选。了解更多

Harsh Parashar

1 Filter methods

Filter methods are based on the statistical properties of the features, such as correlation, variance, or mutual information. They rank the features according to a predefined criterion, and select the ones that meet a certain threshold or a desired number. Filter methods are fast, scalable, and independent of the learning algorithm, but they do not consider the interactions between the features or the relevance to the target variable.

添加您的观点

Harsh Parashar
举报内容
Filter methods are a kind of evaluation method where each feature is evaluated against a criteria. if the criteria are fulfilled, then the feature is selected else rejected. Let's take an example of this method Pearson Correlation Coefficient: This method calculates the linear correlation ([?-1,1]) between each feature and the target variable. Features with high correlation coefficients (positive or negative) are taken as useful features.

已翻译

赞

2 Wrapper methods

Wrapper methods use a learning algorithm to evaluate the features and select the optimal subset that maximizes the prediction accuracy. They search through the feature space using different strategies, such as forward selection, backward elimination, or recursive feature elimination. Wrapper methods are more computationally intensive, prone to overfitting, and specific to the learning algorithm, but they can capture the interactions between the features and the target variable.

添加您的观点

Harsh Parashar
举报内容
There are many wrapper methods that work to identify the relationship between features and target and eliminate non-required features. One of them is given below: Recursive feature elimination (RFE): RFE works by recursively deleting the least significant features based on their weights or coefficients as determined by the algorithm.

已翻译

赞

3 Embedded methods

Embedded methods combine the advantages of filter and wrapper methods, by incorporating the feature selection process into the learning algorithm. They use regularization techniques, such as Lasso, Ridge, or Elastic Net, to penalize the complexity of the model and reduce the coefficients of irrelevant features to zero. Embedded methods are efficient, robust, and generalizable, but they require tuning the regularization parameters and may not work well with noisy or categorical features.

添加您的观点

Harsh Parashar
举报内容
This strategy combines the advantages of both filter and wrapper methods. Initially, it uses filtering techniques to exclude features with minimal potential significance for decision-making. After the filter technique has reduced the feature set, the wrapper method is used to eliminate further features depending on their relevance to the objective. We minimize the number of features effectively by using the filter approach first, followed by the more computationally difficult wrapper method. This sequential technique reduces processing costs by reducing the amount of features vulnerable to the wrapper method's rigorous evaluation.

已翻译

赞

4 Hybrid methods

Hybrid methods are designed to overcome the limitations of the previous techniques, by combining them in different ways. For example, you can use a filter method to reduce the dimensionality of the feature space, and then apply a wrapper or an embedded method to refine the feature subset. Or, you can use an ensemble of different feature selection methods, and select the features that appear most frequently in the results. Hybrid methods are flexible, adaptable, and powerful, but they may also be complex, expensive, and inconsistent.

添加您的观点

Mário Brand?o

Analista de BI | Analista de Dados | Engenheiro de Dados | SQL | Python | AWS | Databricks | Microsoft Azure
举报内容
Refinamento do subconjunto: Aplique um método wrapper, como o Recursive Feature Elimination (RFE), ou um método embedded, como o L1 regularization, para escolher os melhores recursos dentro do subconjunto.

已翻译

赞

5 Dimensionality reduction

Dimensionality reduction is a technique that transforms a high-dimensional feature space into a lower-dimensional one, while preserving as much information as possible. It can be used as a preprocessing step for feature selection, or as an alternative to it. Dimensionality reduction can be performed using linear methods, such as Principal Component Analysis (PCA) or Singular Value Decomposition (SVD), or nonlinear methods, such as t-SNE or UMAP. Dimensionality reduction can improve the computational efficiency, visualization, and noise reduction of your algorithms, but it may also lose some interpretability, variance, or discriminability of your features.

添加您的观点

6 Feature engineering

Feature engineering is a technique that creates new features from existing ones, or from external sources, to enhance the quality and diversity of your data. It can be used as a complement to feature selection, or as a way to generate more features to select from. Feature engineering can be performed using domain knowledge, data transformation, feature extraction, or feature interaction. Feature engineering can improve the performance, robustness, and generalization of your algorithms, but it may also increase the complexity, redundancy, or collinearity of your features.

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

Julius Junior

Data Scientist | Machine Learning & Natural Language Processing | Full Stack Developer
举报内容
Feature engineering is a crucial process for any data scientist. It contributes to the critical success factor of machine learning models. The most popular techniques used in feature selection include: I) Filter methods - The method is majorly used in the data preprocessing stage where a data scientist gets rid of duplicates, redundant features. The method is preferred because it's easier and fast to implement .However it is important to note that the method does not address issues such as multicollinearity which may adversely affect the performance of the model. II) Wrapper method - the method is based on the performance of the best features. III) Embedded method - this is a method that incorporates both methods

已翻译

赞

Computer Engineering

+ 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 are the top techniques for selecting features in machine learning algorithms?

1

2

3

4

5

6

7

1 Filter methods

2 Wrapper methods

3 Embedded methods

4 Hybrid methods

5 Dimensionality reduction

6 Feature engineering

7 Here’s what else to consider

Computer Engineering

给文章评分

感谢您的反馈

更多Computer Engineering相关文章

更多相关阅读内容

What are the top techniques for selecting features in machine learning algorithms?

1

2

3

4

5

6

7

1 Filter methods

2 Wrapper methods

3 Embedded methods

4 Hybrid methods

5 Dimensionality reduction

6 Feature engineering

7 Here’s what else to consider

Computer Engineering

给文章评分

感谢您的反馈

查看其他技能