Classification Trees in Python, From Start To Finish

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In this Guided Project, you will:

Create Classification Trees in Python

Apply Cost Complexity Pruning in Python

Apply Cross Validation in Python

Create Confusion Matrices in Python

Clock2 hours/week
IntermediateIntermediate
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

In this 1-hour long project-based course, you will learn how to build Classification Trees in Python, using a real world dataset that has missing data and categorical data that must be transformed with One-Hot Encoding. We then use Cost Complexity Pruning and Cross Validation to build a tree that is not overfit to the Training Dataset. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your Internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with (e.g. Python, Jupyter, and Tensorflow) pre-installed. Prerequisites: In order to be successful in this project, you should be familiar with Python and the theory behind Decision Trees, Cost Complexity Pruning, Cross Validation and Confusion Matrices. Notes: - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Skills you will develop

Confusion MatrixClassification TreesCost Complexity PruningCross Validation

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Task 1: Import the modules that will do all the work

  2. Task 2: Import the data

  3. Task 3: Missing Data Part 1: Identifying Missing Data

  4. Task 4: Missing Data Part 2: Dealing With Missing Data

  5. Task 5: Format Data Part 1: Split the Data into Dependent and Independent Variables

  6. Task 6: Format the Data Part 2: One-Hot Encoding

  7. Task 7: Build A Preliminary Classification Tree

  8. Task 8: Cost Complexity Pruning Part 1: Visualize alpha

  9. Task 9: Cost Complexity Pruning Part 2: Cross Validation For Finding the Best Alpha

  10. Task 10: Building, Evaluating, Drawing, and Interpreting the Final Classification Tree

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

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Frequently Asked Questions

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