model-comparison-tool

Development

Use when asked to compare multiple ML models, perform cross-validation, evaluate metrics, or select the best model for a classification/regression task.

QUICK START

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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.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