Sentence examples for decision tree generation from inspiring English sources
The second step is the decision tree generation.
Two main steps are proposed which are training and decision tree generation.
Machine learning was applied for decision tree generation by 10-fold cross-validation.
The analysis combines the advantages of statistical approach (regression analysis) and data mining techniques (decision tree) for generation of easily interpretable rules for clinical decision making [ 12].
This important evolution concerning communication tools between healthcare professionals is linked to presentation mode of these recommendations: definition of relevant clinical parameters, inclusion in decision trees, then generation of treatment algorithms; thus, those algorithms can supply databases that are the basic components of each decision-support system.
The same training and evaluation datasets were used to compare the accuracies of a Maximum Likelihood Classifier (MLC) and two new generation decision tree methods, QUEST (Quick Unbiased Efficient Statistical Tree) and CRUISE (Classification Rule with Unbiased Interaction Selection and Estimation), for predicting dominant biological communities.
Table 7(a) shows that a single C4.5 decision tree outperformed DataBoost-IM with data generation in F-score.
11 16 17 Decision trees have been used by Badriyah et al 18 to validate NEWS, though a key difference between the proposed method and the one conducted by Badriyah et al 18 will be the generation of a decision tree that encompasses all vitals rather than a separate tree per vital sign.
Pruning decision trees is a useful technique for improving the generalization performance in decision tree induction, and for trading accuracy for simplicity in other applications.
The algorithm in Figure 19 was repeated for 1, 000 generations and it resulted in a decision tree with a minimum number of 86 leaf nodes and 12 levels for the depth of a tree and was therefore selected.
For the hypotheses generation a recursive partitioning method (actually a simplified version of a decision tree as described in Figure 12) was used to identify fragments that yield the maximum information gain [27].
