Practical Solutions for Model Selection in AI
Value of XGBoost and Deep Learning Models
In solving real-world data science problems, model selection is crucial. Tree ensemble models like XGBoost are traditionally favored for classification and regression for tabular data. Despite their success, deep learning models have recently emerged, claiming superior performance on certain tabular datasets.
Researchers from the IT AI Group at Intel rigorously compared deep learning models to XGBoost for tabular data to determine their efficacy. Evaluating performance across various datasets, they found that XGBoost consistently outperformed deep learning models, even on datasets originally used to showcase the deep models. However, combining deep models with XGBoost in an ensemble yielded the best results, surpassing both standalone XGBoost and deep models. This study highlights that, despite advancements in deep learning, XGBoost remains a superior and efficient choice for tabular data problems.
Comparing GBDTs and Deep Learning Models
Traditionally, Gradient-Boosted Decision Trees (GBDT), like XGBoost, LightGBM, and CatBoost, dominate tabular data applications due to their strong performance. However, recent studies have introduced deep learning models tailored for tabular data, such as TabNet, NODE, DNF-Net, and 1D-CNN, which show promise in outperforming traditional methods.
Ensemble learning, combining multiple models, can further enhance performance. The study thoroughly compared deep learning models and traditional algorithms like XGBoost across 11 varied tabular datasets, revealing that XGBoost consistently outperformed the deep learning models on most datasets. Additionally, ensembles integrating both deep models and XGBoost often yielded superior results compared to individual models or ensembles of classical machine learning models like SVM and CatBoost.
Enhancing Model Performance
The study evaluated the performance of deep learning models on tabular datasets and found them to be generally less effective than XGBoost on datasets outside their original papers. An ensemble of deep models and XGBoost performed better than any single model or classical ensemble, highlighting the strengths of combining methods. XGBoost was easier to optimize and more efficient, making it preferable under time constraints. However, integrating deep models can enhance performance.
If you want to evolve your company with AI, stay competitive, use for your advantage Beyond Deep Learning: Evaluating and Enhancing Model Performance for Tabular Data with XGBoost and Ensembles.
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