Model Comparison Tool

Purpose

• 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.html

Limitations

• 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