ArticleArtificial intelligence review2026
Super greedy trees.
Article in Artificial intelligence review, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
We introduce Super Greedy Trees (SGTs), a decision-tree framework that extends CART by constructing tree splits from lasso-penalized parametric models. At each tree node, a model fitted to the local data induces an adaptive multivariate geometric cut (linear or curved) selected to greedily reduce empirical risk. This yields richer partitions than axis-parallel CART while keeping each split easy to inspect through sparse local structure. In simulated and real-world regression studies, SGTs and an ensemble extension (Super Greedy Forests, SGFs) perform well relative to CART, oblique trees, random forests, and gradient boosted trees, especially when the underlying response surface is complex. In a treadmill ECG and clinical-data case study, SGFs identify sparse combinations of signals associated with long-term survival. The SGT framework thus provides a flexible and theoretically sound approach to tree-based learning.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.