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Oranges-vs.-Grapefruit

Oranges-vs.-Grapefruit

active ARFF CC0: Public Domain Visibility: public Uploaded 23-03-2022 by Onur Yildirim
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  • Computer Systems Machine Learning
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Oranges vs. Grapefruit The task of separating oranges and grapefruit is fairly obvious to a human, but even with manual observation there is still a bit of error. This dataset takes the color, weight, and diameter of an "average" orange and grapefruit and generates a larger dataset containing a wide variety of values and are "oranges" and "grapefruit". Content The dataset is mostly fictional. I'd love to collect real data, but for now measuring starting fruit and creating artificial samples from there seems adequate. Inspiration Binary classification situations are numerous, but tricky for teaching situations. I needed something to create a nice binary classification dataset and still be interesting.

6 features

namestring2 unique values
0 missing
diameternumeric940 unique values
0 missing
weightnumeric6627 unique values
0 missing
rednumeric75 unique values
0 missing
greennumeric80 unique values
0 missing
bluenumeric48 unique values
0 missing

19 properties

10000
Number of instances (rows) of the dataset.
6
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
5
Number of numeric attributes.
0
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
83.33
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.

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