Row 14560

Row ID: 14560 | Dataset Entry | Axioma AXP Content Repository

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A single or a couple of datasets cannot prove anything, because the tabular datasets are highly diverse. The datasets in "Why do tree-based models still outperform deep learning on typical tabular data?" are also biased: they only selected moderately sized datasets. But how about the smaller sized datasets or large-scale datasets?

For a better neural network, please refer to "Excelformer: Can a Deep Learning Model Be a Sure Bet for Tabular Prediction?" It performs comparative or better than GBDTs even require no hyperparameter tuning (if the hyperparameter tuning is applied, the results would be significantly better).

FieldValue
text A single or a couple of datasets cannot prove anything, because the tabular datasets are highly diverse. The datasets in "Why do tree-based models still outperform deep learning on typical tabular data?" are also biased: they only selected moderately sized datasets. But how about the smaller sized datasets or large-scale datasets? For a better neural network, please refer to "Excelformer: Can a Deep Learning Model Be a Sure Bet for Tabular Prediction?" It performs comparative or better than GBD…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-20
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Raw Record

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  "text": "A single or a couple of datasets cannot prove anything, because the tabular datasets are highly diverse. The datasets in \"Why do tree-based models still outperform deep learning on typical tabular data?\" are also biased: they only selected moderately sized datasets. But how about the smaller sized datasets or large-scale datasets?\n\nFor a better neural network, please refer to \"Excelformer: Can a Deep Learning Model Be a Sure Bet for Tabular Prediction?\" It performs comparative or better than GBDTs even require no hyperparameter tuning (if the hyperparameter tuning is applied, the results would be significantly better).",
  "label": "r/machinelearning",
  "dataType": "comment",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-20",
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}
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