The XGBoost Algorithm in Python Video
- Learn about XGBoost (eXtreme Gradient Boosting) along with its benefits.
- Install the XGBoost library.
- Become proficient in installing Anaconda and the XGBoost library on Windows, Linux, and Mac OS.
- Implement the various XGBoost linear and tree learning models in Python.
- Practice applying the XGBoost models using a medical data set.
- Tune the various parameters that exist in Python. Parameter tuning is the art in machine learning.
- Practice applying the three categories of parameter tuning: Tree Parameters, Boosting Parameters, and Other Parameters.
- Become proficient in a number of parameters including max_depth, min_samples_leaf, and max_features.
- Evaluate an XGBoost model.
- Apply the two most important techniques of Train Test Split and Cross Validation. Perform predictions using the XGBoost algorithm.
Instructor: Dhiraj Kumar
Length: 1 hour
Access period: For one year starting from purchase date