Decision Trees, Random Forests & Gradient Boosting in R
What you'll 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
Requirements
- 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.
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!
Who this course is for:
- The ideal students of this course are university students and professionals interested in machine learning and business intelligence.
Course content
- Preview02:53
- 00:06Important: the code is in the resources of lesson 12!
- Preview00:47
- Preview06:14
- 06:17Building a Decision Tree. Part A.
- 07:10Building a Decision Tree. Part B.
- 07:47Building a Decision Tree. Part C.
- 06:55Building a Decision Tree. Part D.
- 3 questions[Assignment] Builidng the Right Side of the Decision Tree
Instructor
![Carlos Martínez, PhD](data:image/svg+xml,%3Csvg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64"%3E%3C/svg%3E){kind=avatar}
- 4.5 Instructor Rating
- 726 Reviews
- 55,946 Students
- 25 Courses
(English profile below)
Carlos es un apasionado del método de casos de enseñanza, coautor de más de 25 de ellos en las áreas de finanzas, estrategia, operaciones y desarrollo sostenible. Algunos de estos han sido incluidos en las bases de casos de prestigiosas escuelas de negocio como Harvard, Michigan e IESE.
Es Ingeniero Industrial y Master en Finanzas de la Universidad Centroamericana, MBA con distinción de INCAE Business School y Ph.D. en Management de la Universidad de San Galo, Suiza. Ha orientado su investigación hacia el impacto de la información social, ambiental, y de gobierno corporativo (ESG) en los mercados financieros. También ha desarrollado estudios relacionados con el acceso al financiamiento emprendedor en contextos con vacíos institucionales.
Su investigación ha sido presentada en prestigiosas conferencias como el coloquio doctoral del Alliance for Research on Corporate Sustainability (MIT, EEUU), la conferencia especializada “From start-up to scale-up” del Academy of Management (University of Tel-Aviv, Israel), la tercera conferencia en Entrepreneurial Finance (Politecnico de Milano, Italia) y el Congreso Anual del European Accounting Association (Valencia, España).
Carlos es coautor de cinco artículos en revistas científicas arbitradas (peer-reviewed) y dos capítulos en libros de editoriales de prestigio (Edward Elgar y World Scientific Publishing). Su investigación ha sido citada por el International Integrated Reporting Council en documentos dirigidos a inversionistas institucionales. Adicionalmente, ha sido revisor ocasional de artículos científicos para varias revistas arbitradas.
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Carlos is passionate about the teaching case method and has co-authored more than 25 of them in the areas of finance, strategy, operations, and sustainable development. Some of these have been included in the case bases of prestigious Universities such as Harvard and Michigan.
He is an Industrial Engineer and Master in Finance from Central American University. He also has an MBA with honors from INCAE Business School, and a Ph.D. in Management from the University of St. Gallen, Switzerland. Carlos has oriented his research towards the impact of social, environmental, and corporate governance (ESG) information on financial markets. He has also developed studies related to access to entrepreneurial financing in contexts with institutional voids.
His research has been presented at relevant conferences such as the doctoral colloquium of the Alliance for Research on Corporate Sustainability (MIT, USA), the specialized conference “From Start-up to Scale-up” of the Academy of Management (University of Tel-Aviv, Israel ), the third conference on Entrepreneurial Finance (Politecnico de Milano, Italy) and the Annual Congress of the European Accounting Association (Valencia, Spain).
Carlos has co-authored five articles in peer-reviewed journals and two chapters in books from prestigious publishers (Edward Elgar and World Scientific Publishing). His research has been cited by the International Integrated Reporting Council in documents aimed at institutional investors. Additionally, he has been an occasional reviewer of scientific articles for several peer-reviewed journals.