Predict the age of abalone from physical measurements
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).
Data comes from an original (non-machine-learning) study:
Warwick J Nash, Tracy L Sellers, Simon R Talbot, Andrew J Cawthorn and Wes B Ford (1994)
"The Population Biology of Abalone (_Haliotis_ species) in Tasmania. I. Blacklip Abalone (_H. rubra_) from the North Coast and Islands of Bass Strait",
Sea Fisheries Division, Technical Report No. 48 (ISSN 1034-3288)
Original Owners of Database:
Marine Resources Division
Marine Research Laboratories - Taroona
Department of Primary Industry and Fisheries, Tasmania
GPO Box 619F, Hobart, Tasmania 7001, Australia
(contact: Warwick Nash +61 02 277277, wnash '@' dpi.tas.gov.au)
Donor of Database:
Sam Waugh (Sam.Waugh '@' cs.utas.edu.au)
Department of Computer Science, University of Tasmania
GPO Box 252C, Hobart, Tasmania 7001, Australia
The evaluation of this dataset is done using Area Under the ROC curve (AUC).
Interpreting the AUROC
Computing the AUROC
Source : http://stats.stackexchange.com/questions/132777/what-does-auc-stand-for-and-what-is-it
One account per participant
No private sharing outside teams
- Use of external data is not permitted. This includes use of pre-trained models.
- Hand-labeling is allowed on the training dataset only. Hand-labeling is not permitted on test data and will be grounds for disqualification.
Please refer to : https://archive.ics.uci.edu/ml/citation_policy.html
Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.