Decision Trees, Boosting, Bagging

(ns index)

1 Assignment 4: Decision Trees, Boosting, Bagging

Instructions

Load the Concrete Compressive Strength Data Set from UCI

  • Use the built-in functions within sklearn to apply the following algorithms and perform analysis using 5-Fold Cross Validation with RMSE as the error function:

  • Decision Tree Regression

  • What is the best method for choosing the number of max features: Auto, Square Root, or Log2?

  • What is the best value for ccp_alpha (the pruning parameter) from the range of 0-1? How deep is the tree with each alpha value?

  • Graphically display the best tree found and list the feature importance associated with it. What variables are most important? What is the Root Node?

  • Random Forest Regression

  • Perform all steps completed for Decision Tree Regression, but don’t graph anything.

  • What is the optimal number of estimators, up to 100?

  • List the feature importance associated with the final model. What variables are most important?

  • Modify the “README.md” file to include the following sections:

  • Questions (Answer these):

  • Which model performed the best in terms of RMSE?

  • Make sure that your README file is formatted properly and is visually appealing. It should be free of grammatical errors, punctuation errors, capitalization issues, etc. Sentences should be complete.