Tabular Foundation Models Can Learn Association Rules

Erkan Karabulut; Daniel Daza; Paul Groth; Martijn C. Schut; Victoria Degeler

cs.AIFeb 16, 2026

Tabular Foundation Models Can Learn Association Rules

Authors: Erkan Karabulut, Daniel Daza, Paul Groth, Martijn C. Schut, Victoria Degeler

Organizations: University of Amsterdam, The Netherlands · Vrije Universiteit Amsterdam, The Netherlands · Amsterdam UMC, location VUmc, Vrije Universiteit Amsterdam, the Netherlands

Abstract

Association Rule Mining (ARM) is a fundamental task for knowledge discovery in tabular data and is widely used in high-stakes decision-making. Classical ARM methods rely on frequent itemset mining, leading to rule explosion and poor scalability, while recent neural approaches mitigate these issues but suffer from degraded performance in low-data regimes. Tabular foundation models (TFMs), pretrained on diverse tabular data with strong in-context generalization, provide a basis for addressing these limitations. We introduce a model-agnostic association rule learning framework that extracts association rules from any conditional probabilistic model over tabular data, enabling us to leverage TFMs. We then introduce TabProbe, an instantiation of our framework that utilizes TFMs as conditional probability estimators to learn association rules out-of-the-box without frequent itemset mining. We evaluate our approach on tabular datasets of varying sizes based on standard ARM rule quality metrics and downstream classification performance. The results show that TFMs consistently produce concise, high-quality association rules with strong predictive performance and remain robust in low-data settings without task-specific training. Source code is available at https://github.com/DiTEC-project/tabprobe.

Figures & tables

Age	Sex	Steroid	Antiviral	$\cdots$	Class
30	female	no	yes	$\cdots$	LIVE
50	male	no	yes	$\cdots$	LIVE
78	male	yes	yes	$\cdots$	LIVE
39	male	yes	no	$\cdots$	DIE
Rule 1	Antiviral = yes $\wedge$ Malaise = yes $\rightarrow$ Class = LIVE
Rule 2	Malaise = yes $\wedge$ Anorexia = yes $\rightarrow$ Class = LIVE

Table 1: Sample clinical dataset and association rules. The table shows sample patient records from a hepatitis dataset with a target outcome. Listed association rules are extracted from TabPFN using our approach and capture interpretable patterns relating symptoms and treatments to patient survival.

Figure 1: Example association rule learning from tabular foundation models using Algorithm 2 : (1) split table into features (X) and labels (Y), (2) fit TFM on X and Y, (3) construct probing matrix with marked items (probability 1) and uniform priors for other items, (4) predict per feature, (5) aggregate predictions into reconstruction matrix, (6) apply thresholding to extract association rules.

Algorithm	# Rules	Support	Confidence	Zhang’s metric	Interestingness	Coverage
Small Tabular Datasets
TabICL	378	38	84.3	31.8	42.6	98.1
TabPFNv2.5	286	38.9	86.3	30.3	43.2	98.1
TabDPT	294	36.6	82.9	26.2	40.9	1
Aerial+	313	28.7	89.3	23.5	34.4	88.1
FP-G (0.3)	430	42.4	89.8	23.6	48.6	99.7

Table 2: Averages of rule quality metrics in percentage across 5 small and 5 larger datasets. Tabular foundation models can learn a concise number of rules with higher association strength, interestingness on average, and full data coverage, with slightly lower confidence scores (FP-G (x): refers to FP-Growth with x being min. support threshold).

Method	# Rules	Accuracy	F1 score	Precision	Recall
Small Tabular Datasets
TabICL	616	82.93	81.07	81.13	82.93
TabDPT	641	82.22	80.35	80.58	82.22
TabPFNv2.5	657	84.47	82.75	83.02	84.47
Aerial+	579	80.69	77.35	76.51	80.69
FP-G (0.3)	266	79.72	76.45	75.84	79.72

Table 3: Predictive performance in percentage and rule (CBA) or itemset (CORELS) size given in column two. TFMs achieve better predictive performance with a concise number of rules in small tabular data, while being behind FP-G by only -1.5% despite FP-G’s rule explosion (FP-G (x) denotes FP-Growth with minimum support threshold

x

Figure 2: Mean execution time on an 8,124-rows dataset (mushroom) as itemset universe size increases. TFMs learn association rules within a minute on modest hardware for small-to-medium tables, whereas only Aerial+ scales to larger sizes (FP-Growth (x): x is min. support threshold).

Appendix figures & tables12 assets

Supplementary material from the paper’s appendix.

Appendix

Dataset	Columns	Items	Rows
Small Tabular Datasets
Acute Inflammations	6	22	120
Hepatitis	19	89	155
Cervical Cancer	19	197	72
ASD Screening (Adolescent)	21	95	104
Fertility	9	36	100

Appendix

Table 4: Small and larger tabular datasets from the UCI ML repository used in all the experimental settings.

Seeds	Values
Seeds 1–4	42, 1608637542, 1273642419, 1935803228
Seeds 5–7	787846414, 996406378, 1201263687
Seeds 8–10	423734972, 415968276, 670094950

Appendix

Table 5: Fixed execution seeds used in all experimental settings for reproducibility of the results.

Method	Parameters
TabPFNv2.5	$a=2$ , $\tau_{a}\in\{0.5,0.3\}$ † , $\tau_{c}=0.8$
TabICL	$a=2$ , $\tau_{a}\in\{0.5,0.3\}$ † , $\tau_{c}=0.8$
TabDPT	$a=2$ , $\tau_{a}\in\{0.5,0.3\}$ † , $\tau_{c}=0.8$
Aerial+	$a=2$ , $\tau_{a}\in\{0.5,0.3\}$ † , $\tau_{c}=0.8$
FP-Growth	$a=2$ , min_conf $=0.8$ , min_supp $\in\{0.3,0.2,0.1,0.05,0.01\}$ ‡

Appendix

Table 6: Parameter settings for TFMs and baselines across experimental settings.

a

is antecedent length. †

\tau_{a}=0.5

for Setting 1,

\tau_{a}=0.3

for Setting 2. ‡ min_supp

\in\{0.3,0.2,0.1\}

for Setting 1,

\in\{0.3,0.2,0.1,0.05,0.01\}

for Setting 2.

Dataset	Batches	Layer Dimensions	Epochs
Small Tabular Datasets
Acute Inflammations	2	4	10
Hepatitis	2	4	10
Cervical Cancer	18	2	20
ASD Screen. (Adolescent)	2	4	10
Fertility	2	4	10

Appendix

Table 7: Optimized Aerial+ architecture and training parameters per dataset; all other parameters kept at default values.

Algorithm	# Rules	Support	Confidence	Zhang’s metric	Interestingness	Coverage
Acute Inflammations
TabICL	46.1	32.9	80.7	57.8	47.3	1
TabPFNv2.5	51.9	31.8	81.2	60.9	46.7	1
TabDPT	52.5	32.4	80.8	60.8	47.5	1
Aerial+	138.2	25.1	87.7	46.3	39.4	1
FP-G(0.3)	46	37.7	92.4	62.1	58.8	1

Appendix

Table 8: Rule quality evaluation of TFMs and baselines on small tabular datasets. In small tabular data, algorithmic ARM (FP-G) still leads to rule explosion. TFMs and Aerial+ can address rule explosion while TFMs find rules of higher statistical significance (Zhang and Interestingness). FP-G(x) refers to FP-Growth with x being min support threshold.

Algorithm	# Rules	Support	Confidence	Zhang’s metric	Interestingness	Coverage
Breast Cancer
TabICL	24.8	49.5	88.7	60.4	58.2	91.2
TabPFNv2.5	27.9	47.4	87.9	57.4	55.6	95.2
TabDPT	27	47.5	88.2	58.5	55.8	91.7
Aerial+	15.7	43.9	88.1	36.4	52.4	90.2
FP-G(0.3)	51	44.4	87.8	32.1	51.4	94.4

Appendix

Table 9: Rule quality evaluation of TFMs and baselines on larger tabular datasets. TFMs can reach full data coverage with up to 12 times fewer rules (Spambase), addressing rule explosion, and lead to rules of higher statistical significance (Zhang and Interestingness). FP-G(x) refers to FP-Growth with x being min support threshold.

Figure 3: Experimental Setting 2 for downstream task evaluation of TFMs and baselines. (1) tabular data is split into training as test folds for cross validation, (2) TFMs and baselines are run on the training fold to learn rules (CBA) or frequent itemsets (CORELS work with frequent itemsets and not rules), (3) learned rules and itemsets are passed to CBA and CORELS to build a rule-based classifier, (4) rule-based classifiers are evaluated on the test fold. This process is repeated 5-fold across 10 random seeds.

Figure 4: Classification tasks results with CORELS on small tabular data. TFMs have better or compatible ( $\pm 1\%$ ) predictive performance on 4 out of 5 small datasets, except the Cervical Cancer dataset. This shows that the small number of rules that TFMs learn is indeed more generalizable than the baselines on unseen data.

Figure 5: Classification task results with CORELS on large tabular data. On large datasets, TFMs have achieved better or comparable performance on all datasets compared to the baselines.

Figure 6: Classification tasks results with CBA on small tabular data. TFMs have better or compatible predictive performance than the baselines in 4 out of 5 datasets, except the Autism Screening dataset, while utilizing a small number of rules.

Figure 7: Classification tasks results with CBA on large tabular data. TFMs resulted in better or compatible predictive performance in only 2 out of the 5 large datasets with CBA. Note that CBA prioritizes rules of higher support and confidence rather than rules of higher association strength and generalizability.

Figure 8: Hyperparameter analysis of Algorithm 2 over all 10 datasets. The left y-axis specifies rule counts, and the right y-axis specifies average rule quality scores. The first row shows that increasing $\tau_{a}$ (while keeping $\tau_{c}=0.8$ fixed) leads to a smaller number of rules, higher support and interestingness, while the association strength (Zhang) and confidence scores do not change significantly. The second row shows that increasing $\tau_{c}$ (while keeping $\tau_{a}=0.5$ fixed) leads to higher support, confidence, association strength, and interestingness scores and reduces the number of rules learned.

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Tabular foundation models (TFMs) have recently emerged as a promising paradigm for machine learning on tabular data, offering the ability to generalize across datasets without task-specific training. Since many machine learning datasets can be represented as tables, this raises the question: does TFM capability extend beyond tasks traditionally regarded as tabular? We address this question by using TabPFN v3 on three non-tabular classification problems: handwritten digit recognition on MNIST, language identification of French and German words, and image classification on Tiny ImageNet. In each case, the original data are represented as rows of a table and classification is formulated as prediction of a missing label. We evaluate performance as a function of the number of context samples provided to the pretrained model, with no additional training or fine-tuning. Despite having no explicit access to the spatial or sequential structure characterizing the data, TabPFN v3 in some cases achieves accuracies comparable with that of models or methods geared specifically toward the corresponding tasks.

Abstract

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