How to compare Bagging ensembles in Python using adult income dataset

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In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in Python programming: How to compare Bagging ensembles in Python using adult income dataset.

What should I learn from this recipe?

You will learn:

  • How to install, load and describe Penn Machine Learning Benchmarks.
  • How to visualise correlation.
  • How to visualise data using seaborn package.
  • How to use machine learning datasets.
  • How to apply sklearn Extra Tree Classifier to adult income data.
  • How to apply sklearn Random Forest Classifier to adult income data.
  • How to apply sklearn Bagging Classifier to adult income data.
  • How to compare Bagging ensembles in Python using adult income dataset
  • Compare results among different classifiers.

 

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Western Australian Center for Applied Machine Learning & Data Science – Membership

 

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Support SETScholars for Free End-to-End Applied Machine Learning and Data Science Projects & Recipes by becoming a member of WA Center For Applied Machine Learning and Data Science (WACAMLDS). Membership fee only $1.75 per month (on annual plan) and you will get access to 475+ end-to-end Python & R Projects.


Western Australian Center for Applied Machine Learning & Data Science – Membership