Machine Learning Random Forest with Python from Scratch - Impurity

The video tutorial introduces the concept of impurity in data sets, explaining its significance in decision tree building. It details how impurity is calculated, focusing on the Gini impurity index. The tutorial provides a step-by-step guide to implementing the Gini impurity calculation in code, emphasizing the importance of minimizing impurity for effective data analysis.

What is impurity in the context of decision trees?

• A measure of data accuracy

• A measure of randomness in a dataset

• A measure of data completeness

• A measure of data consistency

Why is it important to minimize impurity when forming decision nodes?

• To make the tree more complex

• To improve decision-making accuracy

• To reduce the number of nodes

• To increase randomness

Which of the following is NOT a question type used to minimize impurity?

• Diameter is 3 or not

• Color is green or not

• Shape is round or not

• Height is tall or not

What does the Gini impurity formula involve?

• Dividing the probabilities of features

• Adding the probabilities of features

• Subtracting the squared probabilities of features from one

• Multiplying the probabilities of features

What is the purpose of the 'class count' in the Gini impurity calculation?

• To calculate the average impurity

• To provide a dictionary of class names and their occurrences

• To count the number of rows in the dataset

• To determine the total number of features

In the Gini impurity function, what does the 'impurity' variable initially hold?

• The total number of rows

• The value one

• The number of unique classes

• The sum of squared probabilities

What is the final step in the Gini impurity function?

• Returning the class count

• Returning the impurity value

• Calculating the sum of probabilities

• Calculating the total number of rows