Sentence examples for model trees with from inspiring English sources

We introduce ensembles of fuzzified model trees with split attribute randomization and evaluate them for nonlinear dynamic system identification.

Clay minerals were then mapped using data mining model trees with a 10-fold cross validation to derive a mean prediction estimate and 90% prediction interval.

Four single-model learners (CART, k-nearest neighbor, multinomial logistic regression, and logistic model tree) and five ensemble-model learners (CART with bagging, k-nearest neighbor with bagging, multinomial logistic regression with bagging, logistic model trees with bagging, and Random Forest) were compared.

As explained above, this pruning has been made in order to select model trees with low prediction errors.

This number of predictive attributes would generate model trees with huge amounts of nodes, and, therefore, would not be interpretable.

Results reported here were obtained with a threshold value θ = 15%, i.e. the model trees with relative error greater than θ were removed.

We also describe the model implemented for archival and retrieval of SST-1 diagnostics data using an MDSplus server and the model tree, with its sub trees necessitated by the different SST-1 diagnostics system.

Phylogenetic sequence analysis using a consensus model tree with additional p53-like proteins from other species and covering the full length protein sequences support the suggested evolution of the vertebrate p53 protein family (Fig. 3A).

The model tree with the smallest amount of nodes generates the best interpretable models [ 32].

For the scaffold data sets, we used the same technique as in Bininda-Emonds and Sanderson [ 5], and selected a subset of taxa uniformly at random from the model tree, with a fixed probability p, which we called the "scaffold-factor".

For trees having 100, 500 and 1000 taxa, we generated random model trees [ 23], with 30 replicates generated for the 100 and 500 taxon cases, and ten for the 1000 taxon case.