Decision Tree vs Random Forest | Supervised Learning | Day (6/45) | A2Z ML | Mohd Saqib

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In this blog, we will delve deeper into the world of Decision Tree, Random Forest, and explore some common questions that arise during their implementation.

1. How do I choose the root node for my tree?
2. How do I determine the best attribute to split on at each internal node?
3. What should be the criteria for stopping tree growth?
4. Random forest

Index

1. Decision Tree
   - Information Gain, and Gini Index
   - ID3, C4.5, and CART
2. Random Forest

Decision Tree

Choosing the root node:

Information gain and Gini index are two commonly used measures for evaluating the quality of a split in a decision tree. They are used to determine which feature should be selected as the root or internal node.

1. Information Gain:

Information gain measures the decrease in entropy after a dataset is split on an attribute. Entropy is a measure of impurity in the dataset. The attribute with the highest information gain is chosen as the root or internal node.