By the end of this course you should be able to:
- Differentiate uses and applications of classification and classification ensembles.
- Describe and use logistic regression models.
- Describe and use decision tree and tree-ensemble models.
- Describe and use other ensemble methods for classification.
- Use a variety of error metrics to compare and select the classification model that best suits your data.
- Use oversampling and undersampling as techniques to handle unbalanced classes in a data set.