Random forest and its variants are among the most popular algorithms used for solving supervised machine learning challenges involving structured datasets. At the end of this challenge you will understand how it works and you will be able to apply it for your own projects.
- Challenge 1: Calculate gini impurity
- Challenge 2: Split a dataset using gini impurity
- Challenge 3 and 4: Train and test a decision tree
- Challenge 5 and 6: Train and test a random forest, by combining decision trees
Here is a nice blog post for you to read before the session.
This challenge is prepared by Mehmet Alican Noyan.