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December 29, 2017, 01:36:58 PM |
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Algorithm owner and operator are sat on immense external niche market unknown to them.
Even at the best of human skills, this is an inexact science at best.
There is what we call the bias, inserted by the gathering mean and underlying assumptions, data quantity and regularity.
This bias is generally mitigated by the sheer quantity of structured and will often require to clean one’s datasets in order to reduce its effect.
Examples of datasets:
● Iris flower data set – Multivariate data set introduced by Ronald Fisher (1936).[7]
● MNIST database – Images of handwritten digits commonly used to test classification, clustering, and image processing algorithms
● Categorical data analysis – Data sets used in the book, An Introduction to Categorical Data Analysis.
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