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To solve the class prediction problem, we built a classification tree on the learning set, and then sought the optimal placement of each test sample within the classification tree.
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For each subspace of the predictors, we build a classification tree or logistic regression tree.
We also performed a new classification of 584 kinases on the basis of the similarity score which allowed us to build a "classification tree" of the currently known kinases ATP cavities.
CART was used to build a classification tree with the appropriateness score of the panel ratings as a dependent variable, as categorical variable (appropriate, uncertain or inappropriate).
We also implemented this more general approach, as available in the "tree" package of the R statistics software (R Development Core Team 2006), using the standard recursive partitioning model (Foulkes et al. 2004) to build a classification tree.
To further explore how these features relate to H2A/H4R3me2s, we built a single classification tree [ 28], which, compared to the random forests ensemble of trees, may more readily reveal interpretable rules, albeit at the cost of decreased classification accuracy.
Factors included in a resulting tree, as well as its shape, are sensitive to the choice of criteria used to build and prune a classification tree, and this choice is often not straightforward.
To trace the importance of variables responsible for the discrimination of both groups, a classification tree was built.
In parallel the same set of variables were analysed using a classification tree algorithm in order to build classification rules.
Subclassification is performed using a classification tree.
Algorithm ?? describes the basic process of building a classification tree.
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