Single decision tree

To overcome limitations of a single decision tree, especially overfitting, many trees resulting from training with different subsets of the training data set are combined to give a so-called random forest [75]. 

Numerous QSAR tools have been developed [152, 154] and used in modeling physicochemical data. These vary from simple linear to more complex nonlinear models, as well as classification models. A popular approach more recently became the construction of consensus or ensemble models ( combinatorial QSAR ) combining the predictions of several individual approaches [155]. Or, alternatively, models can be built by rurming the same approach, such as a neural network of a decision tree, many times and combining the output into a single prediction. 

Decision Trees are also a well-known technique in the field [151]. They arrange a subset of the descriptor components in a hierarchical fashion (a binary tree) such that on a particular node in the tree a classification on a single descriptor component decides whether the left or the right branch underneath is followed. The leaves of the tree determine the overall classification label. Decision trees have been found useful, especially on large-scale descriptors like binary pharmacophore descriptors [152]. 

DECISION TREE FOR SELECTION OF METHOD FOR SINGLE-PHASE GAS FLOW CAPACITY... 

Decision trees [135] can be used to identify and segment spectra when discriminating rules are known or desired (Fig. 8.8). A binary tree consists of nodes in which a single parameter is used as a discriminant. After a series of nodes are traversed, leaf nodes of the tree are encountered in which all the objects are labeled as belonging to a particular class. Decision trees can be axis parallel or oblique. Axis-parallel trees are called so because they correspond to... 

That is, there is no simple decision tree that links a single, final set of extrapolation methods to a problem definition. 

Combining multiple valid trees that use unique sets of descriptors into a single decision function produces a higher quality model than individual trees. 

Figure 23.7 shows a simple example of a decision tree where the majority of active compounds can be defined by a single set of rules. In more complex trees, one active group of compounds may be defined by a set of rules which state... 

The use of decision analysis can assist in conducting various economic evaluations, including CEA. Although not necessary for all pharmacoeconomic evaluations, decision analysis and decision trees may provide a solid backbone or platform for the decision at hand. Using a decision tree, treatment alternatives, outcomes, and probabilities may be presented graphically and may be reduced algebraically to a single value for comparison (i.e., cost-effectiveness ratio). 

The Hierarchical Tree Substructure Search (HTSS) system [25-27] generates a single rooted decision tree from database structures by gradually refining classification of... 

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