Overall, the combination of computer vision systems with the decision tree algorithm proved to be an effective method for tomato quality classification. Performance metrics including accuracy (0.836), ...

Figure 4. Histogram of bang lenth. 4.2. Decision Boundaries The decision tree model’s reliance on attribute 7 (e.g., Reach) reflects the effectiveness of the model in establishing clear decision ...

1. Introduction Decision tree is a type of supervised learning in machine learning, representing a mapping relationship between sample values and attributes. The decision tree algorithm is easy to ...

This is where GINI Index can be used to create better partitions. However, I am only doing concept implementation here, so I have decided to keep the two parts (main Decision Tree part and GINI Index ...

Decision trees are one of the most widely used models in classification applications. Compared with neural networks and Bayesian methods, decision trees do not need to spend a lot of time and ...

We used a set of methods, including the Pearson correlation coefficient, alternating decision tree (ADTree) (Freund and Mason, 1999; Pfahringer et al., 2001), genetic algorithm (GA) (Whitley, 1994), ...

A 5-fold random cross validation was performed on the dataset. Results: The decision tree model thus constructed successfully classified all 3 tumor types with a performance (AUC) of 0.98 for PCNSLs, ...