LGDTClassifier
LGDTClassifier grows a tree top-down, like CART, but chooses each test with
a two-level lookahead. At each node it computes the best tree of depth 2 for
the rows in the node, keeps only its root test, and repeats on each child.
A single greedy split can miss tests that only pay off one level down (XOR is
the classic example); a depth-2 lookahead does not.
The lookahead uses the same specialised depth-2 solver as DL8.5, which counts the classes of every pair of features once and derives every depth-2 tree from those counts. That keeps LGDT fast enough for deep trees on large datasets, where an optimal search would not finish. Features must be binary.
from pytrees import LGDTClassifier
clf = LGDTClassifier(max_depth=8, min_sup=5).fit(X, y)
Parameters
| Parameter | Default | Description |
|---|---|---|
min_sup | 1 | Minimum number of training rows in each leaf. |
max_depth | 2 | Maximum depth of the tree. At depth 2 or less the tree is optimal (for criterion="error"). |
criterion | "error" | What the depth-2 lookahead optimises: "error" (misclassifications) or "information_gain". |
Fitted attributes
classes_, n_classes_, n_features_in_, tree_ and train_error_, as
described for DL85Classifier. statistics_ holds the error,
duration and sizes of the problem.
Reference
H. Kiossou, P. Schaus, S. Nijssen and G. Aglin. Efficient Lookahead Decision Trees. IDA 2024.