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
Are you interested in mastering the art of building predictive models using machine learning? Look no further than this comprehensive course, "Decision Trees, Random Forests, and Gradient Boosting in R." Allow me to introduce myself, I'm Carlos Martínez, a highly accomplished expert in the field with a Ph.D. in Management from the esteemed University of St. Gallen in Switzerland. My research has been showcased at prestigious academic conferences and doctoral colloquiums at renowned institutions such as the University of Tel Aviv, Politecnico di Milano, University of Halmstad, and MIT. Additionally, I have co-authored over 25 teaching cases, some of which are included in the esteemed case bases of Harvard and Michigan.
This course takes a hands-on, practical approach utilizing a learning-by-doing methodology. Through engaging presentations, in-depth tutorials, and challenging assignments, you'll gain the skills necessary to understand decision trees and ensemble methods based on decision trees, all while working with real datasets. Not only will you have access to video content, but you'll also receive all the accompanying Excel files and R codes utilized in the course. Furthermore, comprehensive solutions to the assignments are provided, allowing you to self-evaluate and build confidence in your newfound abilities.
Starting with a concise theoretical introduction, we will delve deep into the algorithm behind recursive partitioning decision trees, uncovering its inner workings step by step. Armed with this knowledge, we'll then transition to automating the process in R, leveraging the ctree and rpart functions to construct conditional inference and recursive partitioning decision trees, respectively. Additionally, you'll learn invaluable techniques such as estimating the complexity parameter and pruning trees to enhance accuracy and reduce overfitting in your predictive models. But it doesn't stop there! We'll also explore two powerful ensemble methods: Random Forests and Gradient Boosting, which are both built upon decision trees. Finally, we'll construct ROC curves and calculate the area under the curve, providing us with a robust metric to evaluate and compare the performance of our models.
This course is designed for university students and professionals eager to delve into the realms of machine learning and business intelligence. Don't worry if you're new to the decision trees algorithm, as we'll provide an introduction to ensure everyone is on the same page. The only prerequisite is a basic understanding of spreadsheets and R.
Get ready to elevate your skills and unlock the potential to optimize investment portfolios with the power of Excel and R. Enroll in this course today and I look forward to seeing you in class!
Bonus Section: Master Neural Networks for Business Analytics! Unlock the full potential of your decision tree skills with an exclusive bonus section in the Decision Trees course! I've added a comprehensive module covering the application of neural network models in business intelligence. Dive deep into neural network architectures, training techniques, and fine-tuning methods. Plus, get hands-on experience with a real-world case study on credit scoring using actual data. By including this bonus section, I'm providing you with valuable insights into cutting-edge techniques that can revolutionize your data analysis capabilities. Don't miss this opportunity to take your skills to the next level and stand out in the competitive world of business analytics. Enroll now and embrace the power of neural networks in decision-making!
