CSV

We introduce a benchmark of distributed algorithms execution over big data. The datasets are composed of metrics about the computational impact (resource usage) of eleven well-known machine learning techniques on a real computational cluster regarding system resource agnostic indicators: CPU consumption, memory usage, operating system processes load, net traffic, and I/O operations. The metrics were collected every five seconds for each algorithm on five different data volume scales, totaling 275 distinct datasets.

Archival bundle of District Information System for Education (DISE) Delhi primary to upper-primary level schools in academic session 2012-2013. DISE is a school-level dataset consisting of government-recognized schools. It is a joint initiative of the Government of India, UNICEF and the National University of Education and Planning (NUPEA).

Empirical line methods (ELM) are frequently used to correct images from aerial remote sensing. Remote sensing of aquatic environments captures only a small amount of energy because the water absorbs much of it. The small signal response of the water is proportionally smaller when compared to the other land surface targets.

This dataset presents some resources and results of a new approach to calibrate empirical lines combining reference calibration panels with water samples. We optimize the method using python algorithms until reaches the best result.

This in an  artificial imbalanced data set.