Overview

The process of choosing a subset of pertinent features from the initial set of features depending on how important they are in predicting the target variable is known as feature selection. By eliminating features that are superfluous, redundant, or noisy and that could have a detrimental effect on the model’s performance, feature selection aims to increase the performance of machine learning models. Filter methods (e.g., correlation-based feature selection), wrapper methods (e.g., recursive feature removal), and embedding methods (e.g., LASSO regression, decision trees) are examples of feature selection approaches. By using a smaller collection of features, feature selection decreases overfitting, enhances the interpretability of the model, and expedites the training process. The purpose of feature selection is to remove superfluous or unnecessary features from your dataset. The main distinction between feature extraction and feature selection is that the former generates new features, while the latter retains a portion of the original features.

What is Feature Selection?

The process of selecting a subset of features from the original features to minimize the feature space as much as possible while meeting predetermined criteria is known as feature selection. Choosing a subset of pertinent features or variables to utilize in the construction of predictive models is known as feature selection, and it is an essential stage in the machine learning process.

Importance of Feature Selection

Enhanced model performance

You can lessen overfitting, increase the model’s generalizability, and make it easier to understand by choosing only the most pertinent features. Repetitive or unnecessary elements might reduce the accuracy and efficiency of the model.

Faster training and inference

Machine learning models can be trained and inferred more quickly when the number of features is decreased. This is especially crucial when handling complicated models or huge datasets.

Simplicity and interpretability

By concentrating on the most crucial elements, feature selection aids in model simplification. This not only simplifies the model but also sheds light on how the goal variable and characteristics relate to one another.

Reduced dimensionality

The process of feature selection lowers the data’s dimensionality, which helps ease the pain associated with dimensionality. Choosing the most pertinent features might help reduce the computational complexity and performance concerns associated with high-dimensional data.

Cost savings

Gathering and analyzing data might be costly in some situations. You can lower the cost of gathering, storing, and processing data by choosing only the most crucial elements.

Reduction of noise

Adding characteristics to the model that aren’t necessary can cause noise and lower the prediction ability of the model. By removing irrelevant features and concentrating on those that significantly affect the target variable, feature selection assists in reducing noise.

Guidelines for feature engineering

Because feature selection indicates which features are most crucial to the model, it can help guide feature engineering. Data scientists and subject matter experts may find this useful in improving their comprehension of the data and developing new, more helpful features.

Techniques of Filter Selection in Machine Learning

Supervised Learning

Labeled training data is used in supervised learning, a machine-learning approach. This method pairs the intended output with labeled data, which is an input. The relationship between input and output data is something that machines pick up on. From the fresh input data, machines forecast new output by understanding the relationship between them. Regression and classification problems are its finest uses. There are majorly three techniques under Supervised learning feature selection:

• Filter methods
• Wrapper methods
• Embedded methods

Filter Methods

Rather than focusing on cross-validation performance, filter approaches extract the inherent characteristics of the features as assessed by univariate statistics. When compared to wrapper methods, these techniques are less computationally expensive and speedier. When working with high-dimensional data, filter approaches are more computationally affordable.

Statistical Tests

To rank features according to their connection with the target variable, these techniques use statistical measurements such as chi-square, mutual information, or ANOVA. Features that receive low scores may be eliminated.

Entropy-Based Measures

Methods such as information gain or gain ratio evaluate the amount of uncertainty about the target variable that is reduced when a certain feature is included. The majority of prediction power is derived from high-scoring characteristics.

Fisher’s Score

A suboptimal set of features is produced when each feature is independently chosen based on how well it meets the Fisher criterion. The chosen characteristic is better the higher the Fisher’s score.

Dispersion Ratio

For a given feature, the dispersion ratio is the ratio of the arithmetic mean (AM) to the geometric mean (GM). Its value for a given feature varies from +1 to ∞, where AM ≥ GM. A more significant characteristic is implied by a higher dispersion ratio.

Chi-square test

The association between categorical variables is typically examined using the Chi-square method (X2). It does this by comparing the observed values of the dataset’s many properties to their predicted values.

Wrapper Methods

This includes reporting the ideal subset of features utilizing a learning system. One such would be the widespread use of RandomForest in the competitive data science field to assess feature relevance based on information gain. This can help provide some informal validation of engineering features by providing a very fast and dirty summary of which features are relevant. Additionally, resilient to problems like multi-collinearity, missing values, outliers, etc., tree-based models like RandomForest may also identify certain feature relationships. Nevertheless, the computing cost of this can be high.

Forward Selection

Using measurements like accuracy or cross-validation score, start with an empty model and iteratively add the feature that maximizes the model’s performance. Continue until adding more has no more benefits.

Backward Selection

Begin with the entire model and gradually eliminate the feature that affects performance the least. When the model begins to drastically degrade due to removal, stop.

Bi-directional elimination

To arrive at a single, unique answer, this approach concurrently applies the forward selection and backward elimination techniques.

Exhaustive selection

This method is regarded as the brute force approach to feature subset evaluation. It generates every feasible subset, develops a learning algorithm for every subset, and chooses the subset with the greatest model performance.

Embedded Methods

This entails simultaneously performing feature selection and model tuning. Several techniques include models based on Lasso(L1) and Elastic Net(L1+L2), as well as greedy algorithms like forward and reverse selection. To determine when to stop with backward and forward selection, as well as to adjust the parameters for the regularization-based models, will likely need some practice.

Regularization Methods

Regression penalties such as L1 (lasso) or L2 (ridge) automatically reduce coefficients to zero, so removing features that don’t add anything to the model.

Decision Trees

Features that efficiently partition the data during tree construction naturally become more significant in tree-based models. It is possible to identify irrelevant features by analyzing feature importance ratings.

Unsupervised Learning

The opposite of supervised learning is unsupervised learning. This method involves feeding unlabeled data to the system so it may be trained. Without human assistance, it extracts the hidden patterns from the raw or unlabeled data. It works well for jobs involving clustering and exploratory data processing.

Selecting the Appropriate Approach

The type of data, model, and computational resources are some of the variables that determine whether the feature selection method is optimal. Think about the following when making your decision:

• Data Size: While wrapper methods might be computationally expensive, filter methods are typically faster for large datasets.
• Model Complexity: Feature selection is more beneficial for complex models since they are more likely to overfit.
• Interpretability: Compared to wrapper methods, filter methods provide superior feature-importance understanding.

Conclusion

Numerous studies have already been conducted to determine the most effective techniques in the broad and complex field of feature selection. The machine learning engineer’s job is to blend and invent methods, test them, and determine which ones are most effective for the particular issue at hand. Many studies have previously been conducted to determine the most effective techniques in the highly complex and wide-ranging topic of machine learning known as feature selection. The ideal feature selection technique is not predetermined. Ultimately, the decision of which strategy to use depends on a machine learning engineer’s ability to integrate and innovate to find the optimal solution for a given situation. It is advisable to experiment with several model fits on distinct feature subsets chosen using various statistical measures.