Time to stop and smell the flowers! Or was is roses?
This dataset features flowers, and their measurements. Its a popular introductory dataset for data science, and by extension machine learning.
Dataset Structure
| Attribute | Type | Description |
|---|---|---|
| Sepal Length | Numerical () | The length of the outer leaf-like part of the flower. |
| Sepal Width | Numerical () | The width of the outer leaf-like part of the flower. |
| Petal Length | Numerical () | The length of the inner, often colorful, flower petals. |
| Petal Width | Numerical () | The width of the inner, often colorful, flower petals. |
| Species | Categorical | The specific Iris species: Setosa, Versicolor, or Virginica. |
Lab Questions
- Plot sepal_legth vs sepal_width. Then add a random forest regressor and a linear regressor. Does one of them over fit? Which model do you think is better?
- Plot (sepal_length x species) vs sepal_width. Then add a random forest regressor and a linear regressor. Does one of them over fit? Which model do you think is better?
- Plot (sepal_length x sepal_width) vs species. Add two MLPclassifiers, one of them with a scalar, and the other without. Which one performs better?
Dataset Source
The data was originally published in 1936 by the British statistician and biologist Sir Ronald A. Fisher. Link