You can deploy this model and start earning money today!

yimingwang123/
enhanced-readability-random-forest

Name: enhanced-readability-random-forest
Availability: InStock

Views

No views yet

Enhanced Readability Assessment Random Forest Model

This model predicts the reading grade level of English text using an Enhanced Random Forest algorithm with comprehensive linguistic features and improved generalization capabilities.

Model Performance

Cross-Validation MAE: 0.41789318171271833
Training R²: N/A
Test Set R²: 0.8460916091361399
Training Date: 2025-07-02T23:04:35.722721

Enhanced Features

This enhanced model uses 36 total features with 25 selected features using N/A:

Feature Categories:

Traditional Readability Metrics: Flesch-Kincaid, Coleman-Liau, ARI, etc.
Age of Acquisition (AoA) Metrics: Word difficulty based on acquisition age
Syntactic Complexity: Sentence structure and parsing depth
Lexical Diversity: Vocabulary richness and variation
Morphological Features: Word formation patterns
Semantic Features: Word meaning and context complexity
Corpus Source Indicators: Training data source information

Key Improvements:

Feature Selection: Automated selection of most predictive features
Robust Scaling: Better handling of outliers and extreme values
Enhanced Generalization: Optimized hyperparameters for cross-domain performance
Comprehensive Evaluation: Multi-dataset validation

Model Architecture

Algorithm: Random Forest Regressor
Trees: 200 estimators for stability
Max Depth: Controlled to prevent overfitting
Feature Selection: SelectKBest with f_regression
Scaling: RobustScaler for outlier resistance

Usage

python

import joblib
import pandas as pd
import numpy as np

# Load the enhanced model
model = joblib.load('enhanced_readability_random_forest.pkl')

# The model is a complete EnhancedReadabilityRandomForestModel instance
# with built-in feature computation and prediction methods

# Example usage (simplified):
# predicted_grade = model.predict_text("Your text here")

Training Data

Primary: WeeBit corpus (age-graded web content)
Secondary: CLEAR corpus (simplified text pairs)
Validation: Multiple independent datasets
Total Samples: 2500

Performance Comparison

This enhanced model shows improved performance over the baseline:

Better cross-validation stability
Enhanced feature representation
Improved generalization to unseen text types
More robust predictions across grade levels

Citation

If you use this enhanced model, please cite:

Enhanced Readability Assessment Random Forest Model
With Comprehensive Linguistic Features and Improved Generalization
Trained on WeeBit and CLEAR corpus data
2025-07-02

License

MIT License - See LICENSE file for details.

yimingwang123/enhanced-readability-random-forest