Table 1 Features of a cherry (x, y)
From: Constructing phylogenetic networks via cherry picking and machine learning
Num | Feature name | Description |
|---|---|---|
1 | Cherry in tree | Ratio of trees that contain cherry (x, y) |
2 | New cherries | Number of new cherries of \(\mathcal {T}\) after picking cherry (x, y) |
3 | Before/after | Ratio of the number of cherries of \(\mathcal {T}\) before/after picking cherry (x, y) |
4 | Trivial | Ratio of trees with both leaves x and y that contain cherry (x, y) |
5 | Leaves in tree | Ratio of trees that contain both leaves x and y |
Features measured by distance (d) and topology (t) | ||
\(6_{d,t}\) | Tree depth | Avg over trees with (x, y) of ratios “depth of the tree/max depth over all trees” |
\(7_{d,t}\) | Cherry depth | Avg over trees with (x, y) of ratios “depth of (x, y) in the tree/depth of the tree” |
\(8_{d,t}\) | Leaf distance | Avg over trees with x and y of ratios “x-y leaf distance/depth of the tree” |
\(9_{d,t}\) | Leaf depth x | Avg over trees with x and y of ratios “root-x distance/depth of the tree” |
\(10_{d,t}\) | Leaf depth y | Avg over trees with x and y of ratios “root-y distance/depth of the tree” |
\(11_{d,t}\) | LCA distance | Avg over trees with x and y of ratios “x-LCA(x, y) distance/y-LCA(x, y) distance” |
\(12_{d,t}\) | Depth x/y | Avg over trees with x and y of ratios “root-x distance/root-y distance” |