rel. 트리 구조 ~ '의사결정' : cf. as a Decision Architecture in A

 

 

https://machinelearning1.quora.com/Decision-Tree-Classifier

 

 

Decision Tree Classifier

 

• In a decision tree, every decision rule occurs at a decision node, with the rule creating branches leading to new nodes. One reason for the popularity of tree-based models is their interpretability. In fact, decision trees can literally be drawn out in their complete form to create a highly intuitive model.

 

• Decision tree learners attempt to find a decision rule that produces the greatest decrease in impurity at a node. While there are a number of measurements of impurity, by default Decision Tree Classifier uses Gini impurity.

 

• This process of finding the decision rules that create splits to increase impurity is repeated recursively until all leaf nodes are pure or some arbitrary cut-off is reached.

 

• One of the advantages of decision tree classifiers is that we can visualize the entire trained model making decision trees one of the most interpretable models in machine learning.

 

• While this solution visualized a decision tree classifier, it can just as easily be used to visualize a decision tree regressor.