Machine Learning: Random Forest with Python from Scratch - Impurity

The video tutorial introduces the concept of impurity in data sets, explaining its significance in building decision trees. It details how impurity is calculated, emphasizing the importance of minimizing impurity to make effective decisions. The tutorial introduces the Gini impurity, explaining its formula and demonstrating how to implement it in code. The session concludes with a brief mention of information gain, which will be covered in the next lecture.

What is the formula for calculating impurity mentioned in the text?

How does the Gini impurity relate to the concept of impurity in data sets?

What steps are involved in implementing the calculation of impurity in programming?