leaf: Properties of 340 leaves

In dajmcdon/ubc-stat406-labs: Tutorials and labs for UBC Stat 406 in the 2020-2021 online year

Description

A dataset containing attributes of a pile of leaves

Usage

leaf

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 340 rows and 17 columns.

Details

The variables are (roughly) described below. More details as well as the actual images are available at the UCI ML repository.

1. Species - A number from 1 to 36 indicating which species the leaf represents

2. Specimen Number - Numbered sequentially within species

3. Eccentricity - Eccentricity of the ellipse with identical second moments to the image. This value ranges from 0 to 1.

4. Aspect Ratio - Values close to 0 indicate an elongated shape.

5. Elongation - The minimum is achieved for a circular region.

6. Solidity - It measures how well the image fits a convex shape.

7. Stochastic Convexity - This variable extends the usual notion of convexity in topological sense, using sampling to perform the calculation.

8. Isoperimetric Factor - The maximum value of 1 is reached for a circular region. Curvy intertwined con- tours yield low values.

9. Maximal Indentation Depth - How deep indentations are

10. Lobedness - This feature characterizes how lobed a leaf is.

11. Average Intensity - Average intensity is defined as the mean of the intensity image

12. Average Contrast - Average contrast is the the standard deviation of the intensity im- age

13. Smoothness - For a region of constant intensity, this takes the value 0 and approaches 1 as regions exhibit larger disparities in intensity values.

14. Third moment - a measure of the intensity histogram’s skewness

15. Uniformity - uniformity’s maximum value is reached when all intensity levels are equal.

16. Entropy - A measure of intensity randomness.

Source

https://archive.ics.uci.edu/ml/datasets/Leaf

dajmcdon/ubc-stat406-labs documentation built on Aug. 18, 2020, 1:23 p.m.