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Course Outline
Introduction
- Artificial neural networks vs decision tree based algorithms
Overview of XGBoost Features
- Elements of a Gradient Boosting algorithm
- Focus on computational speed and model performance
- XGBoost vs Logistic Regression, Random Forest, and standard Gradient Boosting
The Evolution of Tree-Based Algorithms
- Decision Trees, Bagging, Random Forest, Boosting, Gradient Boosting
- System optimization
- Algorithmic enhancements
Preparing the Environment
- Installing SciPy and scikit-learn
Creating a XGBoost Model
- Downloading a data set
- Solving a common classification problem
- Training the XGBoost model for classification
- Solve a common regression task
Monitoring Performance
- Evaluating and reporting performance
- Early Stopping
Plotting Features by Importance
- Calculating feature importance
- Deciding which input variables to keep or discard
Configuring Gradient Boosting
- Review the learning curves on training and validation datasets
- Adjusting the learning rate
- Adjusting the number of trees
Hyperparameter Tuning
- Improving the performance of an XGBoost model
- Designing a controlled experiment to tune hyperparameters
- Searching combinations of parameters
Creating a Pipeline
- Incorporating an XGBoost model into an end-to-end machine learning pipeline
- Tuning hyperparameters within the pipeline
- Advanced preprocessing techniques
Troubleshooting
Summary and Conclusion
Requirements
- Experience writing machine learning models
Audience
- Data scientists
- Machine learning engineers
14 Hours
