Рет қаралды 27,866
Welcome to How to train XGBoost models in Python tutorial. You'll build an XGBoost Classifier model with an example dataset, step-by-step.
By following this tutorial, you’ll learn:
✅What is XGBoost (vs. gradient tree boosting algorithm)
✅How to build an XGBoost model (Classifier) in Python, step-by-step:
- Step #1: Explore and prep data
- Step #2: Build a pipeline of training
- Step #3: Set up hyperparameter tuning (cross-validation)
- Step #4: Train the XGBoost model
- Step #5: Evaluate the model and make predictions
- Step #6: Measure feature importance (optional)
If you want to use Python to create XGBoost models to make predictions, this practical tutorial will get you started.
GitHub Repo with code: github.com/liannewriting/YouT...
Technologies that will be used:
☑️ JupyterLab (Notebook)
☑️ pandas
☑️ scikit-learn (sklearn)
☑️ category_encoders
☑️ xgboost Python package
☑️ scikit-optimize (skopt)
Links mentioned in the video
► Bank marketing dataset: archive.ics.uci.edu/ml/datase...
► What is gradient boosting in machine learning tutorial: fundamentals explained: www.justintodata.com/gradient...
► To learn Python basics, take our course Python for Data Analysis with projects: www.justintodata.com/courses/...
► sklearn pipeline: scikit-learn.org/stable/modul...
► Target Encoder: contrib.scikit-learn.org/cate...
► XGBClassifier documentation with hyperparameters definition: xgboost.readthedocs.io/en/sta...
There's also an article version of the same content. If you prefer reading, please check it out. How to build XGBoost models in Python: www.justintodata.com/xgboost-...
Get access to more data science materials, check out our website Just into Data: justintodata.com/