An extensive guide to feature selection methods for regression data

Feature selection methods for Regression Data are essential components of data analysis and science. By limiting the amount of input variables in a regression model to just the most informative ones, these techniques simplify the data. The model's performance is improved through this procedure, increasing its accuracy and efficiency.

The objective is to reduce the complexity of the model without compromising its predictive ability by choosing the features that have the greatest influence on the result prediction. These feature selection methods are  vital in the analysis of Regression Data as they are critical in managing high-dimensional data.

Boruta

To rank the features according to their significance and applicability to the target variable, this approach employs random forests.

Variable Importance from Machine Learning Algorithms

To choose the optimal features, this technique takes the feature significance ratings from several machine learning algorithms, such as support vector machines, decision trees, and linear regression.

Lasso Regression

Through the use of a regularization approach, this strategy efficiently eliminates the less significant features from the model by penalizing the feature coefficients and shrinking them to zero.

Stepwise Forward and Backward Selection

Using a greedy search algorithm, this approach adds or eliminates features one at a time by a predetermined criterion, like the Bayesian information criterion (BIC) or the Akaike information criterion (AIC).

Relative Importance of Linear Regression

The relative contribution of each feature to the target variable is calculated using this technique by utilizing the standardized coefficients from a linear regression model.

Recursive Feature Elimination (RFE)

Until the required number of features is obtained, this technique uses a wrapper approach to train a model repeatedly and delete the features with the lowest relevance scores.

Genetic Algorithm

To choose the ideal subset of characteristics that optimizes the performance of the model, this approach employs an optimization strategy inspired by biology and mimicking the process of natural selection.

Variance Threshold

Assuming that the characteristics with low variance have little to no predictive potential, this strategy eliminates them using an unsupervised technique.

Mean Absolute Difference (MAD)

This technique chooses the features with the greatest MAD values by using an unsupervised methodology to estimate the average absolute difference between each feature's values and its mean.

Dispersion Ratio (DR)

By calculating each feature's variance to mean ratio and choosing the features with the greatest DR values, this method employs an unsupervised technique.