H2O AutoML Trains 20 Models to Edge Out SmartKNN by 2%, Data Scientists Question Life Choices

A recent benchmarking study pitted AutoML, a machine learning automation system, against SmartKNN, a single optimized predictive model, on nine datasets across classification and regression tasks. The results showed that SmartKNN, which emphasizes simplicity and interpretability, can hold its ground against state-of-the-art AutoML tools like H2O AutoML. While H2O AutoML achieved higher accuracy in some cases, SmartKNN outperformed it in others, particularly in classification datasets. The study, which took around seven hours to complete using H2O AutoML, demonstrated that SmartKNN can deliver competitive performance with significantly less computational overhead. The datasets used were standard public datasets, and the benchmarks are fully reproducible and available on Kaggle. The study's findings have implications for the industry, suggesting that carefully designed single models can compete with AutoML systems in tabular data scenarios, offering a more efficient and interpretable alternative. The research was conducted using H2O AutoML and SmartKNN, with results showing that SmartKNN can achieve accuracy rates of up to 0.9569 and 0.8036 in certain datasets.