Answer: Removes noise and reduces input size

## Explanation Removing common words (also known as stop words) like "the," "is," and "and" before training a text classification model has several benefits: 1. **Removes noise**: Common words typically don't carry significant semantic meaning for classification tasks and can introduce noise into the model. 2. **Reduces input size**: By eliminating frequent but uninformative words, the vocabulary size decreases, which reduces the dimensionality of the feature space. 3. **Improves computational efficiency**: With fewer tokens to process, training and inference become faster and require less memory. 4. **Focuses on meaningful content**: The model can better focus on words that actually differentiate between different classes or categories. While reducing overfitting (option A) might be a secondary benefit, it's not the main purpose. Removing stop words doesn't improve grammatical accuracy (option B) since grammar isn't typically important for classification tasks. Option D is incorrect because removing words actually decreases token count rather than increasing it. This preprocessing step is common in natural language processing (NLP) and text analytics to create cleaner, more focused datasets for machine learning models.

Author: Ritesh Yadav