FAQ: Multiple Linear Regression - Review

This community-built FAQ covers the “Review” exercise from the lesson “Multiple Linear Regression”.

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

Machine Learning

FAQs on the exercise Review

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!

Anyone else not getting any cheatsheets from the Machine Learning Lessons?

2 Likes

Couple questions:

  1. After playing with removing coefficients I was unable to improve my R^2 score. anyone have any improvement?
  2. The applet is not performing as expected - When I change answers some of items with positive correlation decrease the rent. For example: Changing Has patio and dishwasher to 0 increases rent?
    test file

Let me get this straight:
It doesn’t automatically follow that using the features which have the highest correlations with your target variable will give the best regression model (the R2)?

Why does removing features sometimes improve the accuracy of the model?
Why are we asked to try removing features, or different combinations of features, and not given some kind of methodology, like remove and add features in order of their correlations(Pearson scores) or the value in coef_?

The coef_ method shows the contribution of each feature, why doesnt it just end up the same as the correlation?

guys please check out my repo on the Housing PricePredictor on GitHub:
Housing Price Predictor - Multiple Linear Regression

1 Like

The applet didn’t work at my computer. Anyone had the same problem?

Same here, it’s not working in my browser…