Introduction
Welcome to the session on ‘Hyperparameter Tuning in Decision Trees‘. In the previous session, you learnt about the concepts of homogeneity and its various measures. In this session, you will first understand the disadvantages of decision trees. Then, you will learn about various truncation and pruning strategies that are used to overcome one of the biggest disadvantages of trees, that is, overfitting.
In this session:
- Disadvantages of decision trees
- Tree truncation
- Tree pruning
- Hyperparameter tuning
- Decision tree regression
Guidelines for in-module questions
In-video and in-content questions are not graded. Note that graded questions are given in a separate segment labelled ‘Graded Questions’ at the end of this session. The graded questions in this session will adhere to the following guidelines:
| First Attempt Marks | Second Attempt Marks | |
| Question with 2 Attempts | 10 | 5 |
| Question with 1 Attempt | 10 | 0 |