The Intro to decision tree learning notebook is an in depth tutorial on developing decision tree models for supervised learning tasks. Decision trees are the foundation for boosted algorithms that are known for winning data science competitions.

If you are interested in running this notebook in your own Watson Studio environment, you can access the notebook from GitHub. And for more information on the data set, see the Ames, Iowa: Alternative to the Boston Housing Data as an End of Semester Regression Project article, and then download the data set so you can upload it to your project.

2 comments on"Introduction to Decision Tree Learning"

  1. Hello, it seems that the username:password combination are no longer working. Right after I execute cell #9, this is the error I get:

    UnauthorizedThis server could not verify that you are authorized to access the document you requested.

    Can you help? thank you

    • You’ll need to download the data set to use the notebook. The description at the top of the page includes a link to the notebook, data set, and an article about the data set.

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