Summary GRANDE Gradient-Based Decision Tree Ensembles for Tabular Data arxiv.org
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One Line
GRANDE is an advanced and efficient method for learning decision tree ensembles, incorporating softsign for improved gradient propagation and instance-wise weighting.
Slides
Slide Presentation (12 slides)
Key Points
- GRANDE is a novel approach for learning hard, axis-aligned decision tree ensembles using end-to-end gradient descent.
- GRANDE outperforms existing gradient-boosting and deep learning frameworks on most datasets in terms of predictive performance.
- Tabular data poses challenges such as noise, missing values, class imbalance, and different feature types.
- GRANDE incorporates a differentiable split function called softsign and an instance-wise weighting technique to enhance performance and local interpretability.
- Future work could explore extensions of GRANDE to incorporate categorical embeddings, stacking of tree layers, and integration with deep learning frameworks.
- The article presents performance comparisons and ablation study results using various evaluation metrics and datasets.
- Hyperparameters for each approach are optimized using Optuna with a 5x2 cross-validation.
Summaries
33 word summary
GRANDE is a superior method for learning decision tree ensembles, with improved performance, efficiency, and robustness. It introduces softsign for better gradient propagation and utilizes instance-wise weighting. Extensions of GRANDE could be explored.
59 word summary
GRANDE is a novel method for learning decision tree ensembles using gradient descent. It outperforms existing methods in terms of predictive performance, computational efficiency, and robustness. GRANDE introduces a differentiable split function called softsign, improving gradient propagation and performance. It also incorporates an instance-wise weighting technique for enhanced ensemble performance and interpretability. Future work could explore extensions of GRANDE.
112 word summary
GRANDE is an innovative method for learning decision tree ensembles using end-to-end gradient descent. It combines axis-aligned splits with gradient-based optimization, providing flexibility for handling tabular data. Experimental evaluation on a binary classification benchmark dataset demonstrates that GRANDE outperforms existing methods in terms of predictive performance, computational efficiency, and robustness. It introduces a differentiable split function called softsign, which improves gradient propagation and performance. GRANDE also incorporates an instance-wise weighting technique that enhances ensemble performance and local interpretability. Future work could explore extensions of GRANDE, such as incorporating categorical embeddings and integrating tree layers into deep learning frameworks. The article provides comprehensive evaluation results and details about the hyperparameters for each approach.
292 word summary
GRANDE is a novel approach for learning hard, axis-aligned decision tree ensembles using end-to-end gradient descent. It combines axis-aligned splits with gradient-based optimization, providing flexibility for tabular data. The evaluation of GRANDE on a benchmark dataset of binary classification tasks showed superior predictive performance compared to existing methods.
Tabular data presents challenges such as noise, missing values, class imbalance, and different feature types. Recent studies indicate that tree-based ensemble models outperform deep learning methods for tabular data. Gradient-based methods offer flexibility and the ability to integrate differentiable loss functions tailored towards specific problems.
GRANDE extends the GradTree model to an end-to-end gradient-based tree ensemble while maintaining efficient computation. It introduces a differentiable split function called softsign, which improves gradient propagation and performance. GRANDE also incorporates an instance-wise weighting technique that enhances ensemble performance and local interpretability.
Experimental evaluation demonstrates the superiority of GRANDE in terms of predictive performance, computational efficiency, and robustness. It achieves higher mean macro F1-scores and mean reciprocal ranks compared to other methods, even on large and high-dimensional datasets.
Future work could explore extensions of GRANDE, such as incorporating categorical embeddings, stacking tree layers, and integrating tree layers into deep learning frameworks. These extensions would further enhance the flexibility and performance of GRANDE in handling tabular data.
The article presents comprehensive evaluation results, including accuracy, default parameter performance, runtime, ablation study results, and pairwise confusion matrix comparisons. The hyperparameters for each approach are optimized using Optuna.
In conclusion, GRANDE is a novel approach for learning decision tree ensembles using gradient descent. It outperforms existing methods on various datasets and addresses the challenges of tabular data. Further research and extensions could enhance its flexibility and performance. The article provides comprehensive evaluation results and details about the hyperparameters for each approach.
512 word summary
GRANDE is a novel approach for learning hard, axis-aligned decision tree ensembles using end-to-end gradient descent. It addresses the need for tabular-specific gradient-based methods due to their high flexibility. GRANDE is based on a dense representation of tree ensembles, allowing for the use of backpropagation with a straight-through operator to optimize all model parameters. The method combines axis-aligned splits, which are a useful inductive bias for tabular data, with the flexibility of gradient-based optimization. It also introduces an advanced instance-wise weighting technique that facilitates learning representations for both simple and complex relations within a single model.
The evaluation of GRANDE on a benchmark dataset consisting of 19 binary classification tasks showed that it outperformed existing gradient-boosting and deep learning frameworks on most datasets, demonstrating its superiority in terms of predictive performance. The performance difference between GRANDE and other methods was substantial on several datasets, highlighting the importance of GRANDE as an extension to the existing repertoire of tabular data methods.
Tabular data poses significant challenges such as noise, missing values, class imbalance, and a combination of different feature types. While deep learning has been successful in various domains, recent studies indicate that tree-based ensemble models outperform deep learning methods for tabular data. Gradient-based methods offer advantages over traditional machine learning methods, including flexibility and the ability to integrate arbitrary, differentiable loss functions tailored towards specific problems. Creating tabular-specific gradient-based methods is an active field of research due to the need for well-performing methods.
GRANDE builds upon the work of Marton et al. (2023) by extending GradTree from individual trees to an end-to-end gradient-based tree ensemble while maintaining efficient computation. It introduces a differentiable split function called softsign, which improves the propagation of gradients and leads to better performance compared to other commonly used alternatives such as sigmoid and entmoid. GRANDE also incorporates an instance-wise weighting technique that assigns varying weights to estimators for each sample based on selected leaves. This weighting scheme enhances the performance of the ensemble and improves local interpretability relative to other state-of-the-art methods.
The experimental evaluation of GRANDE demonstrates its superiority over existing methods in terms of predictive performance, computational efficiency, and robustness with default hyperparameters. GRANDE achieved higher mean macro F1-scores and mean reciprocal ranks compared to other popular methods. It also exhibited robust performance on large and high-dimensional datasets.
Future work could explore the extension of GRANDE to incorporate categorical embeddings, stacking of tree layers, and the integration of tree layers into deep learning frameworks. These extensions could further enhance the flexibility and performance of GRANDE in handling tabular data.
The article presents tables comparing the accuracy, default parameter performance, runtime, ablation study results, and pairwise confusion matrix for different approaches on various datasets. The hyperparameters for each approach are optimized using Optuna.
In conclusion, GRANDE is a novel approach for learning decision tree ensembles using gradient descent. It outperforms existing methods on various datasets and addresses the challenges of tabular data. Further research and extensions could enhance its flexibility and performance. The article provides comprehensive evaluation results and details about the hyperparameters for each approach.
775 word summary
GRANDE is a novel approach for learning hard, axis-aligned decision tree ensembles using end-to-end gradient descent. It addresses the need for tabular-specific gradient-based methods due to the high flexibility they offer. GRANDE is based on a dense representation of tree ensembles, allowing for the use of backpropagation with a straight-through operator to optimize all model parameters. The method combines axis-aligned splits, which are a useful inductive bias for tabular data, with the flexibility of gradient-based optimization. It also introduces an advanced instance-wise weighting technique that facilitates learning representations for both simple and complex relations within a single model.
The evaluation of GRANDE was conducted on a benchmark dataset consisting of 19 binary classification tasks. The results showed that GRANDE outperformed existing gradient-boosting and deep learning frameworks on most datasets, demonstrating its superiority in terms of predictive performance. The performance difference between GRANDE and other methods was substantial on several datasets, highlighting the importance of GRANDE as an extension to the existing repertoire of tabular data methods.
Tabular data is widely used and poses significant challenges such as noise, missing values, class imbalance, and a combination of different feature types. While deep learning has been successful in various domains, recent studies indicate that tree-based ensemble models, such as XGBoost and CatBoost, outperform deep learning methods for tabular data. Gradient-based methods offer advantages over traditional machine learning methods, including flexibility and the ability to integrate arbitrary, differentiable loss functions tailored towards specific problems. Creating tabular-specific gradient-based methods is an active field of research due to the need for well-performing methods.
GRANDE builds upon the work of Marton et al. (2023) by extending GradTree from individual trees to an end-to-end gradient-based tree ensemble while maintaining efficient computation. It introduces a differentiable split function called softsign, which improves the propagation of gradients and leads to better performance compared to other commonly used alternatives such as sigmoid and entmoid. GRANDE also incorporates an instance-wise weighting technique that assigns varying weights to estimators for each sample based on selected leaves. This weighting scheme enhances the performance of the ensemble and improves local interpretability relative to other state-of-the-art methods.
The experimental evaluation of GRANDE demonstrates its superiority over existing methods in terms of predictive performance, computational efficiency, and robustness with default hyperparameters. GRANDE achieved higher mean macro F1-scores and mean reciprocal ranks compared to XGBoost, CatBoost, and NODE. It also exhibited robust performance on large and high-dimensional datasets.
Future work could explore the extension of GRANDE to incorporate categorical embeddings, stacking of tree layers, and the integration of tree layers into deep learning frameworks. These extensions could further enhance the flexibility and performance of GRANDE in handling tabular data.
Table 7 shows the accuracy performance comparison of different approaches on various datasets. The test balanced accuracy, along with the mean and standard deviation for a 5-fold cross-validation, is reported. The approaches are ranked in parentheses. The datasets are sorted based on their size.
Table 8 presents the default parameter performance comparison. The test macro f1-score, along with the mean and standard deviation over 10 trials, is reported. The ranking of each approach is also provided. The datasets are sorted based on their size.
Table 9 displays the runtime performance comparison. The runtime, along with the mean and standard deviation for a 5-fold cross-validation, is reported. The ranking of each approach is included. The datasets are sorted based on their size.
Table 10 shows the ablation study split activation results. The test macro F1-Score, along with the mean and standard deviation for a 5-fold cross-validation, is reported. The ranking of each approach is provided. The datasets are sorted based on their size.
Table 11 presents the ablation study weighting results. The test macro F1-Score, along with the mean and standard deviation for a 5-fold cross-validation, is reported. The ranking of each approach is included. The datasets are sorted based on their size.
Table 12 displays the pairwise confusion matrix for the PhishingWebsites dataset. The predictions of each approach are compared with those of a CART DT. It is observed that CART makes more mistakes compared to state-of-the-art models.
The hyperparameters for each approach are optimized using Optuna with 250 trials. The search space and default parameters are selected based on related work. The best parameters are chosen based on a 5x2 cross-validation. Class weights are included to deal with class imbalance. Specific details about the hyperparameters for each approach are provided in Tables 13-17.
In summary, the article compares the performance of different approaches for gradient-based decision tree ensembles on various datasets. The accuracy, default parameter performance, runtime, and ablation study results are reported. The hyperparameters for each approach are optimized using Optuna.