The AmTriangle meta-dataset for experimenting with Supervised Machine Learning
DOI:
https://doi.org/10.25746/ruiips.v9.i4.26211Keywords:
AmTriangle, dataset, Python code, KNN, supervised learningAbstract
This article presents the first version of a meta-dataset named "AmTriangle", and related tools, designed for introducing and experimenting with supervised machine learning.
It is a "meta-dataset", because it is an instrument for generating sets of samples (datasets), as many as desired, correctly classified. Each sample is a triangle, classified as "acute", "obtuse" or "right", according to trigonometry.
A generated dataset can be used to "teach-by-example" how to classify new samples, by different techniques, such as KNN or neural networks; the results depend on options, discussed. The article makes parallels with other datasets: the "triangles" could be other analogous objects.
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