2022
DOI: 10.1155/2022/2231112
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System Optimization of Talent Life Cycle Management Platform Based on Decision Tree Model

Abstract: Decision tree algorithm is a widely used classification and prediction method. Because it generates a tree-like classifier, it has a simple structure and is extensively used by people. Regardless of the decision tree algorithm, the decision attributes are classified according to the condition attributes. The judgment process is from the root node to the leaf node. Each branch of the tree takes the form of selecting the best split attribute. However, this classification method of decision tree makes it rely too… Show more

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Cited by 9 publications
(4 citation statements)
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“…Considering the complexity of data with smaller data sets of learning Random Forest, Decision Tree models are a better choice than XGBoost or deep learning [64,65]. In comparison, Random Forests or Decision Trees are easier to implement in industry due to their simple structure and interpretability [66][67][68].…”
Section: The Results Of Machine Learningmentioning
confidence: 99%
“…Considering the complexity of data with smaller data sets of learning Random Forest, Decision Tree models are a better choice than XGBoost or deep learning [64,65]. In comparison, Random Forests or Decision Trees are easier to implement in industry due to their simple structure and interpretability [66][67][68].…”
Section: The Results Of Machine Learningmentioning
confidence: 99%
“…DT is a classification technique that is frequently used in data mining and ML, thanks to its simplicity. This algorithm analyzes the data using a set of decision rules and generates a tree structure as a result [25]. Each internal node branching from the root node represents a decision rule that tests the value of a feature, and the leaf nodes represent a classification result.…”
Section: Decision Tree (Dt)mentioning
confidence: 99%
“…Decision trees are effective for predicting apple production because they allow for the identification of important factors and the ability to make predictions based on those factors [6]. One limitation of decision trees is that they can be prone to overfitting [7], which occurs when the model is too complex and is able to fit the training data perfectly, but performs poorly on new data. Overfitting can be mitigated by pruning the tree or using techniques such as cross-validation to evaluate the performance of the model on new data.…”
Section: Introductionmentioning
confidence: 99%