Machine Learning Random Forest with Python from Scratch - How to Classify

The video tutorial covers the process of training a model using recursive calls, focusing on decision and leaf nodes. It explains the implementation of the classify method, which determines predictions based on node questions. The tutorial includes running the code, addressing common errors, and testing the model. It concludes with a discussion on future improvements, such as writing helper methods and an accuracy function to evaluate model performance.

What is the base case for the recursive method in a decision tree?

• When the node has no children

• When the node is a leaf node

• When the node has more than two branches

• When the node is a decision node

What should you do if a variable is not defined while running the code?

• Ignore the error and continue

• Remove the variable from the code

• Check for typos and ensure the variable is defined

• Restart the entire code

Why is it important to handle typos in the code?

• They are necessary for debugging

• They improve the readability of the code

• They can cause errors and prevent the code from running

• They make the code run faster

How can you prepare the data for testing a decision tree?

• Exclude the label column from the data

• Use only the first column

• Randomly select columns

• Include all columns including the label

What does it mean if the classify method returns a prediction of 0?

• The person will survive

• The person will not survive

• The model is not trained

• The data is incorrect

What is the next step after implementing the decision tree?

• Delete the existing code

• Write helper methods and an accuracy function

• Deploy the model immediately

• Collect more data

What is the purpose of the accuracy method in a decision tree model?

• To change the model's predictions

• To evaluate the model's performance

• To increase the speed of the model

• To reduce the size of the model