Machine Learning Fundamentals Grid Search and Pipelines
About
An introduction to optimizing machine learning models with grid search and pipelines in scikit-learn.
Learning Objectives
You will be able to:
- Apply
GridSearchCVto systematically search for optimal hyperparameters - Evaluate and interpret grid search results to select the best model configuration
- Implement
scikit-learnpipelines to create reproducible machine learning workflows
Content
| Topic | Skills |
|---|---|
| Slides | - Implement scikit-learn pipelines to create reproducible machine learning workflows |
| Grid Search and Pipelines | - Apply GridSearchCV to systematically search for optimal hyperparameters and use pipelines |
| Practice | - Evaluate and interpret grid search results to select the best model configuration |
Prerequisites
- Interpret and use Python dictionaries.
- Build
pandasDataFramesfrom dictionaries. - Perform simple data manipulation on
pandasobjects. - Understand bias / variance and implement train-test split.
- Machine learning workflow with
scikit-learn.
References
📖 Reference Materials
Important note
Please ignore the _layouts folder and the config.yml file in the root of this repo. They are needed to make sure the repo renders well; please don’t change them or delete them and your repo will work fine :)