ShapDBM: Exploring Decision Boundary Maps in Shapley Space
ShapDBM addresses the fragmentation problem in Decision Boundary Maps (DBMs) by transforming data into Shapley space before applying dimensionality reduction. This creates more compact decision zones that reflect model behavior rather than raw data distribution, enabling high-quality visualization of complex datasets like SVHN where traditional data-space DBMs fail.
The paper presents a novel and theoretically well-motivated approach to improving DBM quality by leveraging Shapley values. The SVHN results are compelling, demonstrating a dramatic improvement in map accuracy from 25.3% to 85.4%. However, the method shows inconsistent benefits—performing worse on MNIST than simple data-space projections—and faces prohibitive computational costs with 18-hour Shapley value computation times that limit practical applicability.
The core theoretical insight—that samples treated similarly by the model naturally cluster in Shapley space—provides a principled solution to the fragmentation problem identified in prior work. The SVHN case study successfully produces the first high-quality DBM for this dataset in the literature, visually demonstrating compact decision zones where data-space methods yield chaotic, fragmented maps. The metric-based evaluation is comprehensive, and the authors honestly acknowledge trade-offs in their inverse projection analysis.
Three issues limit the paper's claims. First, performance is inconsistent: ShapDBM underperforms on MNIST (MA 91.8% vs 96.8%) and produces mixed results on CIFAR-4 (MA 44.4% vs 49.4%), suggesting benefits are limited to cases where data-space DR catastrophically fails. Second, the inverse projection reconstruction quality is significantly degraded in Shapley space, raising validity questions about the synthetic samples despite the authors' argument that sampling farther from original data 'is good for exploring f'. Third, the arbitrary CIFAR-4 subset selection (excluding 6 classes) weakens the evaluation of 'complex' datasets.
The evidence supports the core claim for SVHN but not uniformly across datasets. The comparison is limited by testing only t-SNE in the main text (UMAP relegated to supplements) and using only one CNN architecture. Both methods perform poorly on CIFAR-4 (<50% MA), undermining the claim of handling increasingly complex datasets. Related work is adequately cited, though alternatives to Shapley values for feature importance are not meaningfully compared.
Experimental details are generally sufficient: fixed random seed, hyperparameters specified ($r=500$, $l=1$, 100 DeepExplainer samples), default scikit-learn t-SNE, and a GitHub link for the CNN architecture. However, reproducing the work requires significant computational resources (18 hours for Shapley values on SVHN), and the study provides no containerization, dependency specifications, or code for the full pipeline—only the model architecture is referenced online.
Decision Boundary Maps (DBMs) are an effective tool for visualising machine learning classification boundaries. Yet, DBM quality strongly depends on the dimensionality reduction (DR) technique and high dimensional space used for the data points. For complex ML datasets, DR can create many mixed classes which, in turn, yield DBMs that are hard to use. We propose a new technique to compute DBMs by transforming data space into Shapley space and computing DR on it. Compared to standard DBMs computed directly from data, our maps have similar or higher quality metric values and visibly more compact, easier to explore, decision zones.
Pick a starting point or write your own. Challenges run in the background, so you can keep reading while the AI investigates.
No challenges yet. Disagree with the review? Ask the AI to revisit a specific claim.