Why do tree-based models still outperform deep learning on typical tabular data?
L Grinsztajn, E Oyallon… - Advances in neural …, 2022 - proceedings.neurips.cc
While deep learning has enabled tremendous progress on text and image datasets, its
superiority on tabular data is not clear. We contribute extensive benchmarks of standard and …
superiority on tabular data is not clear. We contribute extensive benchmarks of standard and …
Deep neural networks and tabular data: A survey
Heterogeneous tabular data are the most commonly used form of data and are essential for
numerous critical and computationally demanding applications. On homogeneous datasets …
numerous critical and computationally demanding applications. On homogeneous datasets …
When do neural nets outperform boosted trees on tabular data?
D McElfresh, S Khandagale… - Advances in …, 2024 - proceedings.neurips.cc
Tabular data is one of the most commonly used types of data in machine learning. Despite
recent advances in neural nets (NNs) for tabular data, there is still an active discussion on …
recent advances in neural nets (NNs) for tabular data, there is still an active discussion on …
Regularization learning networks: deep learning for tabular datasets
Despite their impressive performance, Deep Neural Networks (DNNs) typically
underperform Gradient Boosting Trees (GBTs) on many tabular-dataset learning tasks. We …
underperform Gradient Boosting Trees (GBTs) on many tabular-dataset learning tasks. We …
Revisiting deep learning models for tabular data
The existing literature on deep learning for tabular data proposes a wide range of novel
architectures and reports competitive results on various datasets. However, the proposed …
architectures and reports competitive results on various datasets. However, the proposed …
[PDF][PDF] Is deep learning on tabular data enough? An assessment
It is critical to select the model that best fits the situation while analyzing the data. Many
scholars on classification and regression issues have offered ensemble techniques on …
scholars on classification and regression issues have offered ensemble techniques on …
Transfer learning with deep tabular models
Recent work on deep learning for tabular data demonstrates the strong performance of deep
tabular models, often bridging the gap between gradient boosted decision trees and neural …
tabular models, often bridging the gap between gradient boosted decision trees and neural …
On embeddings for numerical features in tabular deep learning
Y Gorishniy, I Rubachev… - Advances in Neural …, 2022 - proceedings.neurips.cc
Recently, Transformer-like deep architectures have shown strong performance on tabular
data problems. Unlike traditional models, eg, MLP, these architectures map scalar values of …
data problems. Unlike traditional models, eg, MLP, these architectures map scalar values of …
Transtab: Learning transferable tabular transformers across tables
Tabular data (or tables) are the most widely used data format in machine learning (ML).
However, ML models often assume the table structure keeps fixed in training and testing …
However, ML models often assume the table structure keeps fixed in training and testing …
Generative table pre-training empowers models for tabular prediction
Recently, the topic of table pre-training has attracted considerable research interest.
However, how to employ table pre-training to boost the performance of tabular prediction …
However, how to employ table pre-training to boost the performance of tabular prediction …