FAQ: Random Forests - Classify

This community-built FAQ covers the “Classify” exercise from the lesson “Random Forests”.

Paths and Courses
This exercise can be found in the following Codecademy content:

Data Science

Machine Learning

FAQs on the exercise Classify

There are currently no frequently asked questions associated with this exercise – that’s where you come in! You can contribute to this section by offering your own questions, answers, or clarifications on this exercise. Ask or answer a question by clicking reply (reply) below.

If you’ve had an “aha” moment about the concepts, formatting, syntax, or anything else with this exercise, consider sharing those insights! Teaching others and answering their questions is one of the best ways to learn and stay sharp.

Join the Discussion. Help a fellow learner on their journey.

Ask or answer a question about this exercise by clicking reply (reply) below!

Agree with a comment or answer? Like (like) to up-vote the contribution!

Need broader help or resources? Head here.

Looking for motivation to keep learning? Join our wider discussions.

Learn more about how to use this guide.

Found a bug? Report it!

Have a question about your account or billing? Reach out to our customer support team!

None of the above? Find out where to ask other questions here!

The list of predictions have None as elements inside it. I am confused why it is so?

Thanks a lot!

3 Likes

I put the following code inside the for loop to print trees which classified unlabeled_point as None.

for i in range(20):
  ...

  prediction = classify(unlabeled_point, subset_tree)
 
  if prediction is None:
    print_tree(subset_tree)

Then I found that the displayed trees cannot actually classify the unlabeled_point (because there are not enough branches). Depending on subset of data it seems possible that the given function build_tree() creates such a tree.

why we use random.seed().Can anyone explain please?thanks you!

If we use the same random.seed, we can reproduce the same results. It is random, but it is the SAME random.

1 Like

thanks you a lot:)!!!

1 Like