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How to train XGBoost models in Python

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: https://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: https://archive.ics.uci.edu/ml/datase... ► What is gradient boosting in machine learning tutorial: fundamentals explained: https://www.justintodata.com/gradient... ► To learn Python basics, take our course Python for Data Analysis with projects: https://www.justintodata.com/courses/... ► sklearn pipeline: https://scikit-learn.org/stable/modul... ► Target Encoder: https://contrib.scikit-learn.org/cate... ► XGBClassifier documentation with hyperparameters definition: https://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: https://www.justintodata.com/xgboost-... Get access to more data science materials, check out our website Just into Data: https://justintodata.com/