- Distribution
- The language's full Yes / No / unk label mix, before the decision tree drops unk rows. Always ordered Yes → No → unk, coloured to match the tree pages. Hover a bar for exact percentages.
- Root Acc
- Smoothed accuracy (+0.5 per class) of always predicting the majority class before any splitting — the root node’s own accuracy. The uninformed baseline. Coloured on the coverage scale between 50% and 100% (teal = high).
- Train Acc
- The decision tree’s accuracy on its own training data: (sum of each leaf’s majority count + 0.5) / (N + 1). Smoothed once for the whole dataset, exactly like Root Acc, so the two are directly comparable and extra leaves are not penalised one by one (the per-leaf
acc shown on the tree page is smoothed per leaf, for the keep test, and so does not average to this). Same colour scale as Root Acc.
- Δ Acc
- Train Acc minus Root Acc, in percentage points; never negative. Coloured on the coverage scale (teal = good) by the share of the root’s remaining error the tree removed, i.e. gain ÷ (100% − Root Acc), clamped to 0–100%. +1 point off a root accuracy of 98% is a much bigger win than +1 point off a root accuracy of 60%.
- Test Acc
- Raw accuracy of the same tree on the held-out split it was not fit on. Compare with Train Acc to spot overfitting: a large gap means the tree learned its training rows more than the pattern. Coloured on the same 50–100% scale, and faded (with a tooltip) when the test split has fewer than 10 rows, since that figure is then too noisy to mean much. Blank when the model has no recorded test split.
- N RAW
- Candidate rows before the leaf-confidence filter is applied. Not coloured. There's no cheap per-language reference point (like treebank size) to scale it against, and not informative enough to be worth adding one for. A language with more than zero but fewer than 10 is left out of this table entirely and listed instead, with its full label distribution, in the "too few or uninformative agreement labels" note above — still real data, just too little of it for this language & condition to draw much of a conclusion from.
- N KEEP
- Of those, the rows kept after the leaf-confidence filter (leaf accuracy > 0.95). The ones actually attempted. Coloured on the coverage scale, as a fraction of N RAW — faded (with a hover tooltip) when N RAW is below 10, since that fraction is too noisy to mean much at that size (2/2 reads as a perfect 100% off pure small-sample luck). The colour still shows, just dimmed, rather than disappearing outright. A language whose strict pass alone wasn't enough and got a laxer, depth-aware retry (sva_trees.second_chance) gets a "retried" tag under its name — N KEEP then reflects that retry's own per-leaf thresholds, whether or not it produced any pairs.
- N PAIRS
- Rows that ended up as a correctly re-inflected minimal pair. Coloured on the coverage scale, as a fraction of N KEEP, with the same under-10 fade as N KEEP above (checked against N KEEP this time, not N RAW). Clickable when example rows are available — opens the same examples modal as the bucket breakdown.