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model-comparison-tool

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Model Comparison Tool

模型对比工具

Compare multiple machine learning models systematically with cross-validation, metric evaluation, and automated model selection.
通过交叉验证、指标评估和自动化模型选择，系统化地对比多个机器学习模型。

Purpose

用途

Model comparison for:
  • Algorithm selection and benchmarking
  • Hyperparameter tuning comparison
  • Model performance validation
  • Feature engineering evaluation
  • Production model selection
模型对比适用于：
  • 算法选择与基准测试
  • 超参数调优对比
  • 模型性能验证
  • 特征工程效果评估
  • 生产环境模型选择

Features

功能特性

  • 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
  • 多模型对比：同时测试5种以上算法
  • 交叉验证：K-fold、分层、时间序列拆分
  • 全面指标：Accuracy、F1、ROC-AUC、RMSE、MAE、R²
  • 统计测试：配对t检验用于显著性分析
  • 可视化：性能图表、ROC曲线、学习曲线
  • 自动选择：根据标准推荐最佳模型

Quick Start

快速开始

python
from model_comparison_tool import ModelComparisonTool
python
from model_comparison_tool import ModelComparisonTool

Compare classifiers

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')
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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')
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CLI Usage

CLI 使用方式

bash
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bash
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Compare models on CSV data

Compare models on CSV data

python model_comparison_tool.py --data data.csv --target target --task classification
python model_comparison_tool.py --data data.csv --target target --task classification

Custom model comparison

Custom model comparison

python model_comparison_tool.py --data data.csv --target price --task regression --models rf,gb,lr --cv 10
python model_comparison_tool.py --data data.csv --target price --task regression --models rf,gb,lr --cv 10

Export results

Export results

python model_comparison_tool.py --data data.csv --target y --output comparison_report.html
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python model_comparison_tool.py --data data.csv --target y --output comparison_report.html
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Limitations

局限性

  • 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
  • 需要足够的数据以实现有意义的交叉验证
  • 大型数据集可能会导致对比耗时较长
  • 不包含深度学习模型（请使用专用框架）
  • 特征工程需提前完成