The impact of data preprocessing on Random Forest model performance for network attacks

The efficacy of machine learning models is highly dependent on the quality of data preprocessing methods employed. This study explores how different preprocessing techniques impact the performance of the Random Forest model. By examining standardization, normalization, handling missing values, and feature selection, we aim to measure their effects on key performance metrics such as accuracy, precision, recall, and F1-score. Using a comprehensive dataset, we systematically compare the performance of the Random Forest model under various preprocessing conditions. Our results indicate that standardization and feature importance ranking significantly boost model performance, while inadequate handling of missing data can lead to notable performance declines. These findings highlight the importance of proper data preprocessing for optimizing Random Forest models, offering practical insights for machine learning practitioners.