Question

in a decision tree, the leaf node represents a ?
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Answer 1

I am having a little trouble understanding the difference between what a "Node" of a tree and a "Leaf" of a tree.

Suppose I am trying to decide the size of coffee a person may like. There are three categories: small, medium, and large based off the peoples age, height, weight, income.

So I have four predictors and 3 possible outcomes. When looking at many gradient boosting algorithms, there are parameters that can increase the number of leaves.

Answer 2

Answer:

The correct answer is: In a decision tree, the leaf node represents a response variable.

Explanation:

A decision tree is an extremely valuable, supervised machine learning technique in which each node represents a predictor variable, the association between nodes represents a decision and each leaf node represents the outcome variable.

Here are some compelling reasons to use decision trees:

• It is often considered the easiest and easiest to understand machine learning algorithm.
• It can be used to solve classification and regression problems.
• Unlike most machine learning algorithms, it works well with nonlinear data.
• Building a decision tree is a fast process because it uses only one feature per node to segment the data.

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