Classification Trees in Python, From Start To Finish

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Neste projeto guiado, você irá:

Create Classification Trees in Python

Apply Cost Complexity Pruning in Python

Apply Cross Validation in Python

Create Confusion Matrices in Python

Clock2 hours
IntermediateIntermediário
CloudSem necessidade de download
VideoVídeo em tela dividida
Comment DotsInglês
LaptopApenas em desktop

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.

Habilidades que você desenvolverá

Confusion MatrixClassification TreesCost Complexity PruningCross Validation

Aprender passo a passo

Em um vídeo reproduzido em uma tela dividida com a área de trabalho, seu instrutor o orientará sobre esses passos:

  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

Como funcionam os projetos guiados

Sua área de trabalho é um espaço em nuvem, acessado diretamente do navegador, sem necessidade de nenhum download

Em um vídeo de tela dividida, seu instrutor te orientará passo a passo

Instrutores

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