This process is called stop word removal in natural language processing (NLP). The main benefits are:

• Removes noise: Common words like "the," "is," and "and" don't typically carry meaningful information for classification tasks
• Reduces input size: By eliminating these frequent but uninformative words, the dataset becomes smaller and more manageable
• Improves model efficiency: With fewer tokens to process, training and inference become faster
• Focuses on meaningful content: The model can concentrate on words that actually contribute to classification decisions

This preprocessing step is particularly important for text classification models to improve performance and reduce computational requirements.