Also, it allows handling of data with missing attribute values and prunes trees after creation. In order to limit excessive partitioning of the data, tree depth (i.e. branching) can be controlled. This is done by so-called tree pruning, which is the process of elimination of unnecessary ...
After the decision tree is built, it can optionally be pruned by Reduced Error Pruning (REP) [Quinlan87] to avoid overfitting. REP assumes that there is a separate pruning dataset, each observation in which is used to get prediction by the original (unpruned) tree. For every non-leaf subtr...
This paper targets on providing data analytics methodology, especially for tree-based data models, in order to support both positive and negative association rules. This work provides several adjusted definitions and expressions for both types of associations rules, and shows simple mathematical results ...
Of the various algorithms in machine learning, decision trees are highly recommended for several reasons, including their ability to handle missing data and their greater interpretability as compared to other algorithms (Costa & Pedreira2023). Decision trees involve multiple analytical strategies, such as...
The paper aims to present an analysis about pruning vegetation with hydraulic pole pruner, a task considered critical, performed by Live Line Electricians (LLE). The research was carried out at an advanced power station, located in the c... FTD Lima,GT Bergstrm,SFB Gemma,... 被引量: 0发...
learn's decision tree classifier, specifying information gain as the criterion and otherwise using defaults. Since we aren't concerned with classifying unseen instances in this post, we won't bother with splitting our data, and instead just construct a classifier using the dataset in its entirety...
This course includes discussions of tree-structured predictive models and the methodology for growing, pruning, and assessing decision trees. In addition, this course examines many of the auxiliary uses of trees such as exploratory data analysis, dimensi
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In this study, we proposed and validated three ensemble models based on the Best First Decision Tree (BFT) and the Bagging (Bagging-BFT), Decorate (Bagging-BFT), and Random Subspace (RSS-BFT) ensemble learning techniques for an improved prediction of flood susceptibility in a spatially-explicit...