Benchmarking Experiment of Machine Learning Algorithms: Systematic Comparison of Classic ML Model Performance

This project was released by haben-ai on GitHub on June 7, 2026 (link: https://github.com/haben-ai/ML_ALGORITHMS_BENCHMARK_EXPERIMENT). It aims to conduct comparative analysis of classic machine learning algorithms' performance across multiple datasets and evaluation metrics through a systematic benchmarking framework, helping to understand the differences between models under varying data characteristics, feature sets, and hyperparameter configurations, and providing data-driven basis for algorithm selection.

In the field of machine learning, choosing the right algorithm is crucial to project success. However, faced with dozens of algorithms (such as logistic regression, random forest, support vector machine, etc.), developers often wonder: 'Which algorithm is best for my data?' This project reveals the relative advantages and disadvantages of each algorithm and the impact of data characteristics (sample size, dimensionality, class distribution) on performance through running multiple algorithms on the same dataset and using consistent evaluation metrics, which is of great value to machine learning practice.

Algorithm Coverage

Covers linear models (linear/logistic regression, ridge regression, Lasso), tree models (decision tree, random forest, Extra Trees), ensemble methods (AdaBoost, Gradient Boosting, XGBoost, LightGBM), support vector machines, K-nearest neighbors, naive Bayes, multi-layer perceptron, and other classic algorithms.

Dataset Diversity

Includes classification (binary, multi-class, imbalanced) and regression task datasets, covering different scales (small/medium/large samples), feature types (numerical/categorical/mixed), and domains (medical, finance, image, etc.).

Evaluation Metrics

• Classification tasks: Accuracy, precision, recall, F1 score, AUC-ROC, log loss, confusion matrix
• Regression tasks: MSE, RMSE, MAE, R², maximum error

Other Methodological Details

Uses K-fold (5/10 fold) stratified cross-validation, repeats experiments to take averages and conducts statistical significance tests; optimizes hyperparameters via grid/random search and analyzes sensitivity; also evaluates computational efficiency such as training time, prediction latency, and memory usage.

Core Patterns

1. No Free Lunch: There is no algorithm that is optimal across all datasets; each algorithm has its own advantages on different data types.
2. Ensemble Method Advantages: Ensemble methods like random forest and gradient boosting perform robustly across multiple datasets, making them 'safe choices' that can effectively reduce overfitting.
3. Impact of Data Scale: Small datasets are suitable for simple models (e.g., logistic regression), large datasets for complex models (e.g., deep learning, gradient boosting), and medium-scale datasets for ensemble methods (best performance).
4. Role of Feature Dimensionality: Most algorithms perform well on low-dimensional data; regularized methods (Lasso/Ridge) and tree models are better for high-dimensional data; some algorithms are sensitive to irrelevant features.
5. Challenge of Class Imbalance: Accuracy can be misleading; need to focus on metrics like precision, recall, F1, or AUC.

Rapid Prototyping Phase

Priority choices: Random forest (general for classification/regression), gradient boosting (XGBoost/LightGBM), logistic regression (as baseline).

Production Environment Deployment

Need to consider: Balance between model complexity and inference speed, interpretability requirements, maintenance costs, hardware resource constraints.

Hyperparameter Tuning Strategy

• Establish a baseline with default parameters
• Use random search for initial exploration
• Perform grid search fine-tuning on well-performing algorithms
• Consider Bayesian optimization to improve efficiency

Application Value

• Educational Learning: Intuitively understand algorithm performance, learn benchmarking practices, and grasp the impact of data characteristics on algorithm selection.
• Industrial Applications: Quickly evaluate candidate algorithms in the early project stage, establish decision-making basis for model selection, and automate model selection processes.
• Research and Development: Provide a fair comparison baseline, identify limitations of existing methods, and guide the direction of new algorithm development.

Limitations

• Insufficient dataset representativeness, unable to cover all scenarios
• Hyperparameter search range limited by computational resources
• Algorithm library version updates may affect results
• Some dataset differences lack statistical significance

Future Expansion

• Incorporate deep learning and AutoML methods
• Add domain-specific datasets
• Evaluate online/incremental learning algorithms
• Support multi-objective optimization (performance + efficiency)
• Develop an interactive web interface to browse results