Authors :
M.Ameerunnisa Begam, M.Guhapriya
Volume/Issue :
Volume 5 - 2020, Issue 2 - February
Google Scholar :
https://goo.gl/DF9R4u
Scribd :
https://bit.ly/2wfyyZb
Abstract :
In this fast-moving world, millions of data
and information exist and accessible to all. But from
those collections, gathering exactly required data leads
to predict accurate results. ML plays a vital role in
converting the data into knowledge. Obliviously people
are interacting with ML every day. From each and
every interaction it constantly learns and improves the
interaction. Regression is an important factor in ML. It
determines the relationship among variables. This
paper provides a study about regression algorithms
such as Linear regression, Support Vector Machine,
Random Forest along with their strengths and
weaknesses.
Keywords :
AI, ML, Regression, Support Vector Machine, Linear regression, Random Forest.
In this fast-moving world, millions of data
and information exist and accessible to all. But from
those collections, gathering exactly required data leads
to predict accurate results. ML plays a vital role in
converting the data into knowledge. Obliviously people
are interacting with ML every day. From each and
every interaction it constantly learns and improves the
interaction. Regression is an important factor in ML. It
determines the relationship among variables. This
paper provides a study about regression algorithms
such as Linear regression, Support Vector Machine,
Random Forest along with their strengths and
weaknesses.
Keywords :
AI, ML, Regression, Support Vector Machine, Linear regression, Random Forest.
