TabPFN and TabICL vs. tuned XGBoost: the model that doesn't train won 14/14

Automated excerpt

Pick any of the six cases I measured to see it with their real data: Default riskSales campaignClinical readmissionRecidivismElectricity demandMolecular screeningThe requestThe contextA single passThe predictionA credit application arrives2 100 applications already settled · 10 columnsclf_num/credit. csvTabICL · nothing tuned · 0. 8 sWill they repay? repaysdefaultsAUC 0. 8528creditA contact enters the list2 100 calls already made · 7 columnsclf_num/bank-marketing. csvTabICL · nothing tuned · 0. 8 sIs it worth calling them? signs updoes notAUC 0. 8699bank-marketingA patient is discharged2 100 previous discharges · 7 columnsclf_num/Diabetes130US. csvTabICL · nothing tuned · 0. 8 sWill they be readmitted? returnsdoes notAUC 0.

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