Text and Data Formatting for Machine Learning

Machine learning is a prominent tool for getting data from large amounts of information. Whereas a good amount of machine learning analysis has targeted on increasing the accuracy and potency of coaching and reasoning algorithms, there is less attention within the equally vital issues of observing t...

Full description

Saved in:
Bibliographic Details
Published in:International journal of innovative technology and exploring engineering 2019-11, Vol.9 (1), p.2756-2760
Main Authors: Chelliah, Dr Balika J., jain, Arth, singh, Utkarsh, Mehta, Garima
Format: Article
Language:English
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Machine learning is a prominent tool for getting data from large amounts of information. Whereas a good amount of machine learning analysis has targeted on increasing the accuracy and potency of coaching and reasoning algorithms, there is less attention within the equally vital issues of observing the standard of information fed into the machine learning model. The standard of huge information is far away from good. Recent studies have shown that poor quality will bring serious errors to the result of big data analysis and this could have an effect on in making additional precise results from the information. Advantages of data preprocessing within the context of ML are advanced detection of errors, model-quality improves by the usage of better data, savings in engineering hours to debug issues
ISSN:2278-3075
2278-3075
DOI:10.35940/ijitee.A5216.119119