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### 2. **Different Preprocessing for Sparse and Dense Documents** You can preprocess sparse and dense documents differently to optimize performance and accuracy. ### 3. **Hybrid Models** Combine different models or techniques to handle sparse and dense documents separately and then integrate the results. ### Example Approach Here's a step-by-step approach to handle a mix of sparse and dense documents: 1. **Identify Sparse and Dense Documents**: Determine which documents are sparse and which are dense. 2. **Preprocess Separately**: Apply different preprocessing techniques to sparse and dense documents. 3. **Feature Extraction**: Use `TfidfVectorizer` or similar techniques to extract features. 4. **Model Training**: Train models that can handle both types of data effectively. ### Example Code Here's an example of how you might implement this approach: ```python import pandas as pd from sklearn.model_selection import train_test_split from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.linear_model import LogisticRegression from sklearn.metrics import recall_score, classification_report, confusion_matrix # Load the data df = pd.read_csv('data.csv')