Machine Learning Random Forest with Python from Scratch - Question and Partition

The video tutorial covers the implementation of helper functions and classes for decision trees. It explains how to create a class for questions, implement a match function, and partition data into true and false categories. The tutorial also discusses the recursive nature of tree structures and hints at the importance of recursion in this context. The session concludes with a promise to explore core theoretical concepts in future lectures.

What is the primary role of a decision node in a decision tree?

• To make a decision between true or false

• To visualize data

• To store data

• To calculate averages

What parameters are required to construct a question in the Question class?

• Row and column

• Column and value

• Type and row

• Value and type

How does the match function determine if a condition is true?

• By comparing the value to a threshold

• By checking if the value is a string

• By calculating the sum of values

• By counting the number of rows

What is the purpose of the partition function?

• To sort data alphabetically

• To calculate statistical measures

• To merge datasets

• To split data into true and false categories

What happens to rows that match the question condition in the partition function?

• They are duplicated

• They are ignored

• They are added to the true rows

• They are discarded

Why is recursion important in the context of decision trees?

• It simplifies data entry

• It allows repeated processing of nodes until a leaf is reached

• It helps in visualizing data

• It enhances data security

What is the outcome when a leaf node is reached in a decision tree?

• The decision-making process ends

• The tree is expanded

• The process starts over

• The data is deleted