The Boruta package implements a novel feature selection algorithm based on random forest. The algorithm iteratively removes features deemed less relevant than random probes. It uses a wrapper approach, comparing real features to random probes to determine relevance. The algorithm is designed to identify all relevant attributes, not just the minimal optimal set. This is important for understanding underlying mechanisms rather than just building predictive models. The algorithm uses a random forest classifier and extends the dataset with shadow attributes to serve as a reference for determining importance. The importance of each attribute is measured by the Z score, which accounts for fluctuations in accuracy loss. The algorithm repeats the process until all attributes are classified as important or unimportant. The Boruta package provides a convenient interface for this process. The algorithm is tested on real and artificial datasets, showing linear scaling with the number of attributes. The package is available in R and can be used for feature selection in machine learning applications. The algorithm is computationally intensive but necessary for statistically significant results. The Boruta package also includes functions for extracting and converting results into convenient formats. The algorithm is demonstrated on the Madelon dataset, where it successfully identifies 20 important attributes. The results show that reducing the number of attributes improves the accuracy of the random forest classifier. The algorithm is also tested using cross-validation to ensure its robustness. The Boruta algorithm is a heuristic method for finding all relevant attributes, including weakly relevant ones. It is useful for feature selection in machine learning applications. The algorithm is available as an R package and has been tested on various datasets. The package provides a convenient interface for the algorithm and is useful for feature selection in machine learning applications. The algorithm is computationally intensive but necessary for statistically significant results. The Boruta package is available in R and can be used for feature selection in machine learning applications. The algorithm is demonstrated on the Madelon dataset, where it successfully identifies 20 important attributes. The results show that reducing the number of attributes improves the accuracy of the random forest classifier. The algorithm is also tested using cross-validation to ensure its robustness. The Boruta algorithm is a heuristic method for finding all relevant attributes, including weakly relevant ones. It is useful for feature selection in machine learning applications.The Boruta package implements a novel feature selection algorithm based on random forest. The algorithm iteratively removes features deemed less relevant than random probes. It uses a wrapper approach, comparing real features to random probes to determine relevance. The algorithm is designed to identify all relevant attributes, not just the minimal optimal set. This is important for understanding underlying mechanisms rather than just building predictive models. The algorithm uses a random forest classifier and extends the dataset with shadow attributes to serve as a reference for determining importance. The importance of each attribute is measured by the Z score, which accounts for fluctuations in accuracy loss. The algorithm repeats the process until all attributes are classified as important or unimportant. The Boruta package provides a convenient interface for this process. The algorithm is tested on real and artificial datasets, showing linear scaling with the number of attributes. The package is available in R and can be used for feature selection in machine learning applications. The algorithm is computationally intensive but necessary for statistically significant results. The Boruta package also includes functions for extracting and converting results into convenient formats. The algorithm is demonstrated on the Madelon dataset, where it successfully identifies 20 important attributes. The results show that reducing the number of attributes improves the accuracy of the random forest classifier. The algorithm is also tested using cross-validation to ensure its robustness. The Boruta algorithm is a heuristic method for finding all relevant attributes, including weakly relevant ones. It is useful for feature selection in machine learning applications. The algorithm is available as an R package and has been tested on various datasets. The package provides a convenient interface for the algorithm and is useful for feature selection in machine learning applications. The algorithm is computationally intensive but necessary for statistically significant results. The Boruta package is available in R and can be used for feature selection in machine learning applications. The algorithm is demonstrated on the Madelon dataset, where it successfully identifies 20 important attributes. The results show that reducing the number of attributes improves the accuracy of the random forest classifier. The algorithm is also tested using cross-validation to ensure its robustness. The Boruta algorithm is a heuristic method for finding all relevant attributes, including weakly relevant ones. It is useful for feature selection in machine learning applications.