Data
abalone

abalone

active ARFF CC BY 4.0 Visibility: public Uploaded 22-12-2022 by Sebastian Fischer
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Data Description Predicting the age of abalone from physical measurements. The age of abalone is determined by cutting the shell through the cone, staining it, and counting the number of rings through a microscope -- a boring and time-consuming task. Other measurements, which are easier to obtain, are used to predict the age. Further information, such as weather patterns and location (hence food availability) may be required to solve the problem. From the original data examples with missing values were removed (the majority having the predicted value missing), and the ranges of the continuous values have been scaled for use with an ANN (by dividing by 200). An instance of this dataset is an abalone that was cut to determine the age. Wiki entry on abalone, can be found [here][1]. [1]: Attribute Description 1. *sex* - sex of the abalone, possible values include M, F, and I (infant) 2. *length* - longest shell measurement in mm 3. *diameter* - perpendicular to length in mm 4. *height* - height with meat in shell in mm 5. *whole_weight* - whole abalone weight in grams 6. *shucked_weight* - weight of meat in grams 7. *viscera_weight* - gut weight (after bleeding) in grams 8. *shell_weight* - weight after being dried in grams 9. *rings* - the age in years of abalone, target feature

9 features

rings (target)numeric28 unique values
0 missing
sexnominal3 unique values
0 missing
lengthnumeric134 unique values
0 missing
diameternumeric111 unique values
0 missing
heightnumeric51 unique values
0 missing
whole_weightnumeric2429 unique values
0 missing
shucked_weightnumeric1515 unique values
0 missing
cisvera_weightnumeric880 unique values
0 missing
shell_weightnumeric926 unique values
0 missing

19 properties

4177
Number of instances (rows) of the dataset.
9
Number of attributes (columns) of the dataset.
0
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.
8
Number of numeric attributes.
1
Number 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.
-1.1
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
88.89
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
11.11
Percentage of nominal attributes.

1 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: rings
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