Implementation of decision tree algorithms in python
Answers
Answered by
0
Decision trees are a powerful prediction method and extremely popular.
They are popular because the final model is so easy to understand by practitioners and domain experts alike. The final decision tree can explain exactly why a specific prediction was made, making it very attractive for operational use.
Decision trees also provide the foundation for more advanced ensemble methods such as bagging, random forests and gradient boosting.
In this tutorial, you will discover how to implement the Classification And Regression Tree algorithm from scratch with Python.
After completing this tutorial, you will know:
How to calculate and evaluate candidate split points in a data.How to arrange splits into a decision tree structure.How to apply the classification and regression tree algorithm to a real problem.
They are popular because the final model is so easy to understand by practitioners and domain experts alike. The final decision tree can explain exactly why a specific prediction was made, making it very attractive for operational use.
Decision trees also provide the foundation for more advanced ensemble methods such as bagging, random forests and gradient boosting.
In this tutorial, you will discover how to implement the Classification And Regression Tree algorithm from scratch with Python.
After completing this tutorial, you will know:
How to calculate and evaluate candidate split points in a data.How to arrange splits into a decision tree structure.How to apply the classification and regression tree algorithm to a real problem.
Similar questions