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  • 5/11/2025
Data evaluation is the process of assessing the quality, relevance, and accuracy of data to determine its suitability for analysis and decision-making. It involves checking if data is accurate, relevant, and ready for analysis, essentially linking raw data to meaningful insights.
Transcript
00:00hola guys and gullies welcome back
00:30if you have a regression model
00:33you have decided to decide
00:37regression model
00:38regression model
00:40evaluation criteria
00:42basically
00:44your customer
00:46will tell you
00:48that
00:50basically check
00:52one person's health condition
00:54or not
00:55imagine
00:56health condition
00:57this evaluation criteria
01:01that
01:02your model
01:03make sure
01:05that
01:0695%
01:07accurate
01:08not
01:09then
01:10many people
01:11wrong
01:12this
01:13one thing
01:14is
01:15a model
01:16project
01:17which
01:18is defined
01:19accuracy
01:20now
01:21accuracy
01:22which
01:23is
01:24different
01:25parameters
01:26for example
01:27you have
01:28regression
01:29now
01:30regression
01:31you have
01:32basically
01:33mean absolute
01:34abs
01:35error
01:36this
01:37is
01:38mean absolute
01:39error
01:40this
01:41is
01:43mean square
01:44error
01:45mean square
01:46error
01:47this
01:48is
01:49root mean square
01:50error
01:51you have
01:52you have
01:53you have
01:54you have
01:55you have
01:56you have
01:57you have
01:58you have
01:59you have
02:00for example
02:01you have
02:02mean square
02:03error
02:0495%
02:05accuracy
02:06achieve
02:07let's say
02:0815%
02:09imagine
02:10if
02:11mean square
02:12error
02:1315%
02:14you have
02:15you have
02:16you have
02:17you have
02:18you have
02:19this
02:20model
02:21acceptable
02:22this
02:23classification
02:24and regression
02:25are
02:26different
02:27criteria
02:28now
02:29this
02:30criteria
02:31for
02:32you have
02:33a school
02:34example
02:35to
02:36see
02:37what
02:38evaluation
02:39is
02:40percentage
02:41number
02:42of
02:43subject
02:44percentage
02:45you have
02:46step up
02:47university
02:48then
02:49evaluation
02:50criteria
02:51grade
02:52point
02:53average
02:54evaluation
02:55criteria
02:56which
02:57you have
02:58changed
02:59model
03:00to
03:01model
03:02change
03:03and
03:04evaluation
03:05criteria
03:06make sure
03:07your
03:08model
03:09accuracy
03:10limit
03:11that
03:12is
03:13excellent
03:14here
03:15we have
03:16this
03:17lecture
03:18to
03:19do
03:20do
03:21do
03:22do
03:23do
03:24do
03:25do
03:26do
03:27do
03:28do
03:29do
03:30do
03:31do
03:32do
03:33do

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