How to do Feature Selection – remove highly correlated features in R

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How to do Feature Selection – remove highly correlated features in R

When working with a large dataset, it’s common to have features that are highly correlated with each other. These correlated features provide redundant information to the model and can negatively impact the performance. To overcome this issue, we can use feature selection techniques to remove highly correlated features.

One way to remove highly correlated features is by using the findCorrelation() function from the caret package in R. This function calculates the correlation coefficient between all the features in the dataset and returns a correlation matrix. From the matrix, we can identify the features that are highly correlated and remove them.

Another way is to use the Boruta package that uses random forest to rank features by importance and also uses permutation importance for feature selection. This package also allows us to set a threshold value for the correlation. All features above this threshold will be removed.

A third approach is to use the Recursive Feature Elimination (RFE) method in combination with correlation matrix. The correlation matrix is used to identify highly correlated features and RFE is used to eliminate the least important features from the dataset.

In summary, When working with a large dataset, it’s common to have features that are highly correlated with each other, providing redundant information to the model and negatively impacting the performance. To overcome this issue, we can use feature selection techniques to remove highly correlated features. One way is by using the findCorrelation() function from the caret package in R, another way is by using the Boruta package and setting a threshold value for correlation, and a third approach is to use the Recursive Feature Elimination (RFE) method in combination with correlation matrix. These methods allow us to identify and remove correlated features, helping to improve the performance of the model.

 

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: How to do Feature Selection – remove highly correlated features in R.



How to do Feature Selection – remove highly correlated features in R

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