Decision Tree Intuition

Decision Tree Intuition

Decision Tree Intuition. In Programming, we regularly use If-else conditions, even the Decision Tree working process is similar to an If-else condition.

Decision Trees are easy & Simple to implement & interpreted. Decision Tree is a diagram (flow) that is used to predict the course of action or a probability. Each branch of the decision tree represents an outcome or decision or a reaction. Decision Trees can be implemented in a variety of situations from personal to complex situations. The sequence of steps will give a better understanding easily.

In Programming, we regularly use If-else conditions, even the Decision Tree working process is similar to an If-else condition.

Let’s see how a decision Tree Looks like

The below tree shows a simple implementation of different nodes in the decision tree.

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  1. *Root Node: *Root Node is a top node with the base feature.
  2. *Parent Node: *Nodes that get their origin from a root node or this can be represented as a decision node where the decision of Yes/No or True/False or prediction turn happens.
  3. *Child Node: *these nodes get their origin from a parent node. If the decision made from a parent node is not satisfactory then these nodes will be created. Until we arrive at the final node where we have pure domination in a class (Yes/No) means that until we arrive at the leaf node.
  4. *Leaf Node: *Can also be called a terminal Node or a final decision node where we will conclude.

gini-index entropy decision-tree decision-tree-classifier machine-learning

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