Introduction to
Decision Trees
Decision Tree is a Supervised learning technique that can be used for
both classification and Regression problems, but mostly it is preferred for
solving Classification problems.
In a Decision tree, there are two nodes, which are the Decision Node and
Leaf Node.
It is a graphical representation for getting all the possible solutions to a
,
, Decision Tree Terminolo
1 • Root Node: The top node of the decision tree, whi
• Internal Nodes: Nodes that represent the features
decisions.
• Leaf Nodes: The final nodes of the tree that repres
class labels.
• Branches: The paths connecting the nodes, represe
each step.
• Depth: The number of levels or nodes from the ro
the complexity of the tree.
• Pruning: The process of removing unnecessary bra
overfitting and improve generalization.
Decision Trees
Decision Tree is a Supervised learning technique that can be used for
both classification and Regression problems, but mostly it is preferred for
solving Classification problems.
In a Decision tree, there are two nodes, which are the Decision Node and
Leaf Node.
It is a graphical representation for getting all the possible solutions to a
,
, Decision Tree Terminolo
1 • Root Node: The top node of the decision tree, whi
• Internal Nodes: Nodes that represent the features
decisions.
• Leaf Nodes: The final nodes of the tree that repres
class labels.
• Branches: The paths connecting the nodes, represe
each step.
• Depth: The number of levels or nodes from the ro
the complexity of the tree.
• Pruning: The process of removing unnecessary bra
overfitting and improve generalization.
