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--- |
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title: Enhanced Readability Assessment Random Forest Model |
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emoji: 🌲 |
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colorFrom: green |
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colorTo: blue |
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sdk: gradio |
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sdk_version: 4.0.0 |
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app_file: app.py |
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pinned: false |
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tags: |
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- readability |
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- nlp |
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- education |
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- random-forest |
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- sklearn |
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- enhanced-features |
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- linguistic-analysis |
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license: mit |
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--- |
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# Enhanced Readability Assessment Random Forest Model |
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This model predicts the reading grade level of English text using an Enhanced Random Forest algorithm with comprehensive linguistic features and improved generalization capabilities. |
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## Model Performance |
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- **Cross-Validation MAE**: 0.41789318171271833 |
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- **Training R²**: N/A |
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- **Test Set R²**: 0.8460916091361399 |
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- **Training Date**: 2025-07-02T23:04:35.722721 |
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## Enhanced Features |
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This enhanced model uses **36** total features with **25** selected features using **N/A**: |
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### Feature Categories: |
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- **Traditional Readability Metrics**: Flesch-Kincaid, Coleman-Liau, ARI, etc. |
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- **Age of Acquisition (AoA) Metrics**: Word difficulty based on acquisition age |
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- **Syntactic Complexity**: Sentence structure and parsing depth |
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- **Lexical Diversity**: Vocabulary richness and variation |
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- **Morphological Features**: Word formation patterns |
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- **Semantic Features**: Word meaning and context complexity |
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- **Corpus Source Indicators**: Training data source information |
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### Key Improvements: |
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- **Feature Selection**: Automated selection of most predictive features |
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- **Robust Scaling**: Better handling of outliers and extreme values |
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- **Enhanced Generalization**: Optimized hyperparameters for cross-domain performance |
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- **Comprehensive Evaluation**: Multi-dataset validation |
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## Model Architecture |
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- **Algorithm**: Random Forest Regressor |
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- **Trees**: 200 estimators for stability |
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- **Max Depth**: Controlled to prevent overfitting |
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- **Feature Selection**: SelectKBest with f_regression |
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- **Scaling**: RobustScaler for outlier resistance |
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## Usage |
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```python |
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import joblib |
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import pandas as pd |
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import numpy as np |
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# Load the enhanced model |
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model = joblib.load('enhanced_readability_random_forest.pkl') |
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# The model is a complete EnhancedReadabilityRandomForestModel instance |
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# with built-in feature computation and prediction methods |
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# Example usage (simplified): |
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# predicted_grade = model.predict_text("Your text here") |
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``` |
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## Training Data |
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- **Primary**: WeeBit corpus (age-graded web content) |
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- **Secondary**: CLEAR corpus (simplified text pairs) |
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- **Validation**: Multiple independent datasets |
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- **Total Samples**: 2500 |
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## Performance Comparison |
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This enhanced model shows improved performance over the baseline: |
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- Better cross-validation stability |
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- Enhanced feature representation |
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- Improved generalization to unseen text types |
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- More robust predictions across grade levels |
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## Citation |
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If you use this enhanced model, please cite: |
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``` |
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Enhanced Readability Assessment Random Forest Model |
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With Comprehensive Linguistic Features and Improved Generalization |
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Trained on WeeBit and CLEAR corpus data |
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2025-07-02 |
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``` |
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## License |
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MIT License - See LICENSE file for details. |
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