GRANDE: Gradient-Based Decision Tree Ensembles for Tabular Data

Core Concepts

GRANDE introduces a novel approach for learning hard, axis-aligned decision tree ensembles using end-to-end gradient descent, outperforming existing methods on most datasets.

Abstract

GRANDE proposes a new method for tabular data, combining axis-aligned splits with gradient-based optimization. It outperforms existing models and enhances local interpretability through instance-wise weighting.
Despite the success of deep learning in various domains, tree-based ensemble models remain state-of-the-art for machine learning with heterogeneous tabular data. GRANDE introduces a novel approach called GRAdieNt-Based Decision Tree Ensembles (GRANDE) that leverages end-to-end gradient descent to learn hard, axis-aligned decision tree ensembles. This method combines the advantageous inductive bias of tree-based methods with the flexibility of gradient-based optimization. The authors conducted an extensive evaluation on 19 classification datasets and demonstrated that GRANDE outperforms existing gradient-boosting and deep learning frameworks on most datasets. The proposed method is available under https://github.com/s-marton/GRANDE.
Heterogeneous tabular data presents significant challenges such as noise, missing values, class imbalance, and different feature types. Despite the success of deep learning in various domains, recent studies indicate that tree-based models like XGBoost and CatBoost outperform them in most cases when dealing with tabular data. Employing end-to-end gradient-based training provides several advantages over traditional machine learning methods by offering flexibility through easy integration of differentiable loss functions tailored towards specific problems and supporting iterative training. Creating tabular-specific, gradient-based methods is an active field of research due to the intense need for well-performing methods.
The proposed GRANDE method extends GradTree from individual trees to an end-to-end gradient-based tree ensemble while maintaining efficient computation. It introduces softsign as a differentiable split function and proposes a novel weighting technique that emphasizes instance-wise estimator importance. An extensive evaluation on 19 binary classification tasks based on a predefined benchmark demonstrates that GRANDE outperforms existing methods for both default and optimized hyperparameters.

Stats

Extensive evaluation conducted on 19 classification datasets.
Outperforms existing gradient-boosting and deep learning frameworks.
Available under https://github.com/s-marton/GRANDE.
Proposed method combines axis-aligned splits with gradient-based optimization.

Quotes

"Despite the success of deep learning (DL) in various domains, recent studies indicate that tabular data still poses a major challenge." - Borisov et al., 2022
"The proposed GRANDE method extends GradTree from individual trees to an end-to-end gradient-based tree ensemble while maintaining efficient computation." - Marton et al., 2023

Key Insights Distilled From

GRANDE

by Sasc... at arxiv.org 03-13-2024

https://arxiv.org/pdf/2309.17130.pdf

Deeper Inquiries

How does the introduction of instance-wise weighting impact model performance compared to traditional approaches

Instance-wise weighting in GRANDE impacts model performance by allowing varying weights for each estimator based on the instance being evaluated. This approach enhances ensemble diversity, enabling individual trees to focus on different areas of the target function. Unlike traditional methods that use a single weight for each estimator, instance-wise weighting emphasizes learning representations for both simple and complex rules within a single model. This leads to improved performance as it simplifies the task for other estimators in the ensemble, making them more efficient at capturing unique local interactions.

What are the implications of combining axis-aligned splits with gradient-based optimization for future developments in machine learning

The combination of axis-aligned splits with gradient-based optimization in GRANDE has significant implications for future developments in machine learning. By utilizing hard, axis-aligned decision trees along with flexible gradient-based training, GRANDE strikes a balance between interpretability and flexibility. This approach allows the model to capture non-linear relationships efficiently while avoiding overly smooth solutions often associated with soft oblique splits used in some DL architectures like NODE.
This innovative fusion opens up possibilities for developing models that can handle tabular data effectively while maintaining robustness and interpretability. The success of this approach suggests potential applications beyond tabular data where similar challenges exist regarding feature importance interpretation and non-linear relationship modeling.

How can the success of GRANDE be generalized beyond tabular data applications

The success of GRANDE can be generalized beyond tabular data applications due to its fundamental principles and advantages over existing methods. By leveraging end-to-end gradient-based training combined with hard, axis-aligned decision trees, GRANDE offers a unique blend of interpretability and flexibility suitable for various machine learning tasks.
One key aspect that can be applied across domains is the concept of instance-wise weighting to enhance ensemble diversity and improve model performance by focusing on specific instances' characteristics rather than treating all samples equally. Additionally, the use of axis-aligned splits ensures that models do not favor overly smooth solutions common in some deep learning approaches but instead learn piece-wise constant functions suited for capturing complex relations efficiently.
Overall, these principles can be adapted and extended to diverse domains such as image classification, natural language processing (NLP), time series analysis, or any application requiring interpretable yet flexible models capable of handling intricate relationships within data sets.