Synthetic records across multiple Iris species classes. SMOTE-based augmentation provides an enlarged sample set for multi-classification model training.
Use Cases
- Train a multi-class classifier using the Iris labels and Iris feature columns.
- Evaluate the effect of SMOTE-based synthetic data generation on classification accuracy.
- Perform cluster analysis on the enlarged feature set to identify Iris groupings.
Strengths
- Includes synthetic samples generated via SMOTE to expand the standard Iris dataset row count.
- Contains Iris feature columns for multi-classification.
- Categorizes data into multiple Iris labels.