Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
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