Updated 3 years ago
Gradient Boosting Machines (GBMs) with XGBoost
This tutorial is a part of Machine Learning with Python: Zero to GBMs and Zero to Data Science Bootcamp by Jovian
The following topics are covered in this tutorial:
- Downloading a real-world dataset from a Kaggle competition
- Performing feature engineering and prepare the dataset for training
- Training and interpreting a gradient boosting model using XGBoost
- Training with KFold cross validation and ensembling results
- Configuring the gradient boosting model and tuning hyperparamters
Let's begin by installing the required libraries.
!pip install numpy pandas matplotlib seaborn --quiet
!pip install jovian opendatasets xgboost graphviz lightgbm scikit-learn xgboost lightgbm --upgrade --quiet