Which Node function is used to automatically grow a decision tree in the Interactive Decision Tree?

The function used to automatically grow a decision tree in the Interactive Decision Tree is the Train function. When you utilize the Train function, it initiates the learning process where the algorithm examines the training data to identify patterns and relationships. This process results in the construction of the decision tree, where nodes represent decision points based on the features of the data.

During this training phase, various criteria, including measures of impurity like Gini index or entropy, are applied to determine how to split the data at each node. The process continues iteratively, growing the tree until certain stopping criteria are met, such as a minimum node size or maximum depth of the tree. This algorithmic approach allows the decision tree to capture the complexities of the data, ultimately improving predictive accuracy.

The other functions listed do not specifically pertain to the tree growth process. Data nodes handle the input and manipulation of datasets before any model training occurs, while Regression and Classify nodes are typically used for model building in their respective contexts but do not specifically address the automatic growth of a decision tree.