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!
Content Benefits Details
- 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.
- 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 & Testing
- Section Introduction
- Decision Trees with CTREE
- Interpretation of Results
- Prediction with CTREE
- Confusion Matrix
- ROC Curve
- Area under the ROC curve (AUC)
- Section Introduction
- Decision Trees with RPART
- Choosing Complexity Parameter
- ROC and AUC RPART
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.
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