What is Top-Down Induction

1.

A recursive method of decision tree generation. It starts with the entire input dataset in the root node where a locally optimal test for data splitting is searched and branches corresponding to the test outcomes are created. The test searches and data splitting are repeated in the created nodes unless the stopping condition is met. Learn more in: Evolutionary Algorithms for Global Decision Tree Induction

2.

A recursive method of decision tree generation. It starts with the entire input dataset in the root node where a locally optimal test for data splitting is searched and branches corresponding to the test outcomes are created. The test searches and data splitting are repeated in the created nodes unless the stopping condition is met. Learn more in: Evolutionary Algorithms for Global Decision Tree Induction

3.

A recursive method of decision tree generation. It starts with the entire input dataset in the root node where a locally optimal test for data splitting is searched and branches corresponding to the test outcomes are created. The test searches and data splitting are repeated in the created nodes unless the stopping condition is met. Learn more in: Relative Relations in Biomedical Data Classification

4.

A recursive method of decision tree generation. It starts with the entire input dataset in the root node where a locally optimal test for data splitting is searched and branches corresponding to the test outcomes are created. The test searches and data splitting are repeated in the created nodes unless the stopping condition is met. Learn more in: Global Induction of Classification and Regression Trees