As I work on practicing and solving machine learning (ML) problems, I repeatedly find myself duplicating a set of steps and activities.
Thanks to Dr. Jason Brownlee’s suggestions on creating a machine learning template, I have pulled together a set of project templates that I use to experiment with modeling ML problems using Python and XGBoost.
Version 2 of the XGBoost templates contain minor adjustments and corrections to the prevision version of the template. The updated templates also include:
- Scikit-learn’s ColumnTransformer, imputing, and pipeline utilities for feature scaling and transformation tasks
You will find the Python templates on the Machine Learning Project Templates page.