Decision Trees, Random Forests & Gradient Boosting in R

BY
Udemy

Mode

Online

Fees

₹ 2699

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study
Mode of Delivery Video and Text Based

Course and certificate fees

Fees information
₹ 2,699
certificate availability

Yes

certificate providing authority

Udemy

The syllabus

Introduction

  • Welcome to the course
  • Important: the code is in the resources of lesson 12!
  • Section Introduction
  • Introduction to Decision Trees
  • Building a Decision Tree. Part A.
  • Building a Decision Tree. Part B.
  • Building a Decision Tree. Part C.
  • Building a Decision Tree. Part D.
  • [Assignment] Builidng the Right Side of the Decision Tree

Data Preprocessing

  • Section Introduction
  • Teaching Case: Edutravel
  • Describing the Dataset
  • Importing CSV Data into R
  • Changing the Data Type
  • Dealing with Missing Data
  • Combining Rare Categories
  • Data Split: Training and Testing Datasets

Decision Tree with CTREE

  • Section Introduction
  • Decision Tree with CTREE
  • Interpretation of Results
  • Prediction with the CTREE Model
  • Confusion Matrix
  • ROC Curve
  • Area Under the ROC Curve (AUC)
  • Test 1

Decisions Trees with RPART

  • Section Introduction
  • Decisions Trees with rpart
  • Choosing Complexity Parameter
  • Classification and Confusion Matrix
  • ROC and AUC

Random Forests

  • Section Introduction
  • Theotrical Introduction to Random Forests
  • Building a Random Forest Model in R
  • Classification and Confusion Matrix
  • ROC & AUC
  • [Assignment] Playing with the cutoff value

Gradient Boosting Trees

  • Section Introduction
  • Theoretical Introduction to Gradient Boosting
  • XGBoost Model
  • Prediction and Confusion Matrix
  • [Assignment] ROC & AUC
  • Conclusion

Instructors

Mr Carlos Martinez
Industrial Engineer
Freelancer

Other Masters, Ph.D, MBA

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