Machine Learning Fundamentals Imbalanced Data
About
Understand the challenges of imbalanced data and apply techniques to address class imbalance in datasets.
Learning Objectives
You will be able to:
- Describe when imbalanced data is a problem.
- Identify methods for handling imbalanced data and appropriately evaluating performance.
- Apply the
imbalanced-learnlibrary to oversample minority classes. - Apply the
imbalanced-learnlibrary to create synthetic minority class observations.
Content
| Topic | About |
|---|---|
| Slides | - Describe when imbalanced data is a problem |
| 02 Imbalanced Classes | - Identify methods for handling imbalanced data and appropriately evaluating performance - Apply the imbalanced-learn library to oversample minority classes- Apply the imbalanced-learn library to create synthetic minority class observations |
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, includingGridSearch
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 :)