05 Tree Based Models
Preparation¶
Chapters 6 and 7.
You will need to install the graphviz and pydotplus modules in python. You can do this by following these steps:
Open the anaconda prompt Install graphviz by typing "conda install python-graphviz" Install pydotplus by typing "conda install pydotplus"
Material¶
Session Description¶
We will cover tree-based models.
Learning Objectives¶
- Use and implement decision trees, random forests and gradient boosted decision trees in python.
- Describe the advantages and disadvantages of using decision trees, random forests and gradient boosted decision trees, respectively.
- Visualize decision trees in different ways.
- Extract and interpret feature importance.
- Describe how the Gini impurity index can be used to determine which feature to branch off on.
- Explain what is meant by pre-pruning.
- Explain how random forests are random, including what is meant by bootstrapping and feature selection in this context.
- Explain what is meant by soft voting.
- Discuss different hyperparameters of tree-based methods, and how tuning these parameters influence the results.