… This study aimed to evaluate and compare the prediction capability of the naïve Bayes tree
(NBTree), alternating decision tree (ADTree), and random forest (RF) methods for the spatial …

… The PCA result feature is used as input for the Decision Tree C4. 5 classification algorithm.
The experimental results show that using the training and testing data distribution percentage …

… of using decision trees is logarithmic in the number of data points used to train the tree,
making predictions efficient even with large datasets. Furthermore, decision trees are versatile, …

… This study used different decision trees obtained from different split criteria. In another
study, a decision tree, data mining technique, was used to investigate the factors impacting the …

… of two-class boosted decision tree and two-class decision forest algorithms in predicting fake
… two-class boosted decision tree is healthier than the Two class decision forest algorithms in …

… of class given information higher in the tree. This chapter will only be concerned with class
probability trees since decision trees are a special case. Tree-based approaches have been …

… a Decision Tree Classification Model makes decisions and performs splits based on impurity
metrics. The Decision tree … By visually representing the decision-making process, it offers …

… and implementation of Decision Tree (DT) training … decision tree to grow to its full depth
(decided by user). Afterwards, pruning conditions are applied for prevention of over-fitting of tree…

… predicted (with decision tree analyses, it is not possible to calculate negative and positive
predictive values for the entire “tree”). Finally, we performed a decision tree analysis after …

… , 24; in particular, the decision-tree algorithm may help clinicians estimate … decision-making.
Many previous studies have already shown the effectiveness of CPRs using a decision-tree …