• Post category:StudyBullet-2
  • Reading time:9 mins read

Learn to build predictive models with machine learning, using different Rstudio´s packages: ROCR, caret, XGBoost, rparty

What you will learn

The algorithm behind recursive partitioning decision trees

Construct conditional inference decision trees with R`s ctree function

Construct recursive partitioning decision trees with R`s rpart function

Learn to estimate Gini´s impurity

Construct ROC and estimate AUC

Random Forests with R´s randomForest package

Gradient Boosting with R´s XGBoost package

Deal with missing data

Description

Would you like to build predictive models using machine learning? That´s precisely what you will learn in this course “Decision Trees, Random Forests and Gradient Boosting in R.” My name is Carlos Martínez, I have a Ph.D. in Management from the University of St. Gallen in Switzerland. I have presented my research at some of the most prestigious academic conferences and doctoral colloquiums at the University of Tel Aviv, Politecnico di Milano, University of Halmstad, and MIT. Furthermore, I have co-authored more than 25 teaching cases, some of them included in the case bases of Harvard and Michigan.

This is a very comprehensive course that includes presentations, tutorials, and assignments. The course has a practical approach based on the learning-by-doing method in which you will learn decision trees and ensemble methods based on decision trees using a real dataset. In addition to the videos, you will have access to all the Excel files and R codes that we will develop in the videos and to the solutions of the assignments included in the course with which you will self-evaluate and gain confidence in your new skills.

After a brief theoretical introduction, we will illustrate step by step the algorithm behind the recursive partitioning decision trees. After we know this algorithm in-depth, we will have earned the right to automate it in R, using the ctree and rpart functions to respectively construct conditional inference and recursive partitioning decision trees. Furthermore, we will learn to estimate the complexity parameter and to prune trees to increase the accuracy and reduce the overfitting of our predictive models. After building the decision trees in R, we will also learn two ensemble methods based on decision trees, such as Random Forests and Gradient Boosting. Finally, we will construct the ROC curve and calculate the area under such curve, which will serve as a metric to compare the goodness of our models.

The ideal students of this course are university students and professionals interested in machine learning and business intelligence. The course includes an introduction to the decision trees algorithm so the only requirement for the course is a basic knowledge of spreadsheets and R.

I hope you are ready to upgrade yourself and learn to optimize investment portfolios with excel and R. I´ll see you in class!

English

Language

Content

Introducción

Welcome to the Course!

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.

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


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Data Split: Training and Testing Datasets

Decisions Trees with CTREE

Section Introduction

Decisions Trees with CTREE

Interpretation of Results

Prediction with the CTREE Model

Confusion Matrix

ROC Curve

Area Under the ROC Curve (AUC)

Test 1

Decisions Tress with RPART

Section Introduction

Decisions Trees with rpart

Choosing Complexity Parameter

Classification and Confusion Matrix

ROC and AUC

Random Forests

Section Introduction

Theoretical Introduction to Random Forests

Building a Random Forest Model in R

Classification and Confusion Matrix

ROC & AUC

Gradient Boosting Trees

Section Introduction

Theoretical Introduction to Gradient Boosting

XGBoost Model

Prediction and Confusion Matrix

Conclusion