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 does impurity measure in a dataset?

• The amount of order

• The amount of randomness

• The number of rows

• The number of columns

Why is it important to minimize impurity at the root node of a decision tree?

• To ensure the best question is asked

• To maximize randomness

• To reduce the number of columns

• To increase the number of questions

What is the formula used to calculate impurity?

• 1 minus the sum of squared probabilities

• Product of probabilities

• Sum of probabilities

• Difference of probabilities

What is another name for Gini impurity?

• Gini index

• Gini score

• Gini coefficient

• Gini ratio

In the coding example, what does the 'class count' function return?

• A count of rows

• A list of class names

• A dictionary with class names and their occurrences

• A single class name

What is the initial value of impurity set to in the coding example?

• Zero

• One

• The number of rows

• The number of columns

What operation is performed on the probability of each label in the coding example?

• Division

• Squaring

• Subtraction

• Addition