In static concurrent prediction, TabPFN outperformed Random Forest, XGBoost and Linear Regression across the four emotional and proficiency targets¶

Claim · All claims
Evidence · 1 study · associational r1 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Cross-validated comparison of four models showed TabPFN consistently outperformed Random Forest, XGBoost and Linear Regression, particularly for anxiety and boredom; the static TabPFN explained 65.1% of variance in enjoyment, 59.6% in boredom, 51.5% in self-perceived proficiency, and 45.8% in anxiety. → Peng Wang 2026

Evidence¶

Peng Wang 2026¶

Peng Wang, Lesya Ganushchak, Camille Welie, Roel van Steensel. (2026). Emotions Are Not Random: Machine Learning Reveals Predictable Patterns in a 21-Day Second Language Learning Trajectory. Educational Psychology Review. https://doi.org/10.1007/s10648-026-10141-8

q2 · i? · associational · r1

Cross-validated static concurrent prediction (Fig. 2) comparing TabPFN, Random Forest, XGBoost and Linear Regression on identical feature sets; Linear Regression lagged notably behind, which the authors attribute to non-linear relationships.

"Across all targets, TabPFN consistently outperformed the alternative models, particularly for more complex emotional states such as anxiety and boredom."

Discussion¶

Related Claims¶

• Time-series forecasting with TabPFN-TS markedly improves prediction of next-episode emotions and perceived proficiency over a static model (e.g., anxiety R² 0.458→0.866; boredom 0.596→0.953) — related
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• Machine learning methods outperformed logistic regression in classification accuracy across reviewed studies — related