- 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
Online
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Quick facts
particular | details | |
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course and certificate fees
Fees information
certificate availability
certificate providing authority
The syllabus
Introduction
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