Model Comparison Tool
Compare multiple machine learning models systematically with cross-validation, metric evaluation, and automated model selection.
Purpose
Model comparison for:
- Algorithm selection and benchmarking
- Hyperparameter tuning comparison
- Model performance validation
- Feature engineering evaluation
- Production model selection
Features
- Multi-Model Comparison: Test 5+ algorithms simultaneously
- Cross-Validation: K-fold, stratified, time-series splits
- Comprehensive Metrics: Accuracy, F1, ROC-AUC, RMSE, MAE, R²
- Statistical Testing: Paired t-tests for significance
- Visualization: Performance charts, ROC curves, learning curves
- Auto-Selection: Recommend best model based on criteria
Quick Start
from model_comparison_tool import ModelComparisonTool
# Compare classifiers
comparator = ModelComparisonTool()
comparator.load_data(X_train, y_train, task='classification')
results = comparator.compare_models(
models=['rf', 'gb', 'lr', 'svm'],
cv_folds=5
)
best_model = comparator.get_best_model(metric='f1')CLI Usage
# Compare models on CSV data
python model_comparison_tool.py --data data.csv --target target --task classification
# Custom model comparison
python model_comparison_tool.py --data data.csv --target price --task regression --models rf,gb,lr --cv 10
# Export results
python model_comparison_tool.py --data data.csv --target y --output comparison_report.htmlLimitations
- Requires sufficient data for meaningful cross-validation
- Large datasets may have long comparison times
- Deep learning models not included (use dedicated frameworks)
- Feature engineering must be done beforehand