Sentence examples for maximal tree depth from inspiring English sources

During the generation of the current RP tree, only physicochemical properties were used, the maximal tree depth was four, the minimal number of samples per node was 40, and the weighting method was by class.

TA minimal gain of 0.1 to produce a split, a maximal tree depth of 20, a confidence level of 0.25 for the pessimistic error calculation of pruning and the number of alternative nodes of 3 when pre-pruning would prevent a split.

The models run with a minimal size of four for a node to allow a split, a minimal size of two for all leaves, a minimal gain of 0.1 to produce a split, a maximal tree depth of 20, a confidence level of 0.25 for the pessimistic error calculation of pruning and the number of alternative nodes of three when prepruning would prevent a split.

The models were run with the minimal size of 4 for a node to allow a split, a minimal size of 2 for all leaves, a minimal gain of 0.1 to produce a split, a maximal tree depth of 20, and a confidence level of 0.25 for the pessimistic error calculation of pruning and the number of alternative nodes of 3 when prepruning would prevent a split.

For models run with a minimal size of 4 for a node to allow a split, a minimal size of 2 for all leaves, a minimal gain of 0.1 to produce a split, and a maximal tree depth of 20, the confidence level of 0.25 for the pessimistic error calculation of pruning and the number of alternative nodes of 3 when prepruning would prevent a split.

Pruning of the trees (to correct for overtraining) was undertaken using the 1 SE rule described by [ 15] in combination with a maximal tree-depth of 4 layers.

RP Single Trees had a minimum of ten samples per node and a maximum tree depth of 20.

b Max tree depth with accuracy.

During each iteration of the algorithm, the decoder may move forward (increase depth within the tree), move backward (reduce depth), or stay at the current tree depth.

The two trees also differ in their tree depth.

The depth of the tree is a fixed parameter in the variational algorithm (whereas in the original MCMC method the tree depth changes dynamically during the sampling).