00:00Hello guys, welcome back.
00:05So, now we have our fifth feature, which is basically modeling.
00:10Modeling is what is called?
00:11Modeling is three parts.
00:14The first part is that you have to choose and train.
00:20Model choose and train.
00:23Train and choose model.
00:25Part one is called.
00:26Part two is that you have to find.
00:30Tune the model.
00:36And third part is that you have to compare.
00:41Model compare.
00:44Comparison or compare.
00:46These three things you have to be in mind.
00:49Modeling is three basic components.
00:51Modeling is first.
00:53Model choose and tune.
00:54Then you have to compare.
00:56Modeling is first.
00:57The same thing you have to choose.
01:00If you have turned to a machine learning learning page.
01:02To be patient, that is the basic unit.
01:05If you have three tasks.
01:06It doesn't matter.
01:07To be patient, of course.
01:08You have to try to train.
01:09The same thing that you have to do.
01:10If you have three tasks as a team,
01:10So if you have three tasks,
01:13if you have three tasks for three tasks,
01:15whether you have model training or tune or model compare,
01:20you have to do data.
01:21So this means that you have to do data set.
01:28And remember that the data set is different,
01:32one data set is same.
01:34So if you have to say 100 patients,
01:39if you have to say 70 patients,
01:43you have to do data and then you have to do data.
01:48Then I will tell you that 15% of the data you have to do data.
01:55And then 15% of the model test.
02:00Make sense?
02:02Three stages are.
02:04Train, tune and model comparison.
02:07When you train, 70% of the data you have to do model training.
02:13Toon, 15% normal practice.
02:16But normal 70, 15% of the data you have to do.
02:20Now I will repeat it so that you have to do confusion.
02:26So this example you have to do a course that you have to do.
02:30prepare you university in the middle of the semester.
02:32Now normal is that you have to study
02:34courses and study courses.
02:36Then when exam comes,
02:38you have to train
02:40all the semester.
02:42You have to train
02:44all the semester.
02:46Then exam
02:48you have to test exam.
02:52You have to practice exam.
02:54This question, exercise
02:56that I have done.
02:58You have to validate
03:00you.
03:02Validation data set
03:04is called
03:06validation
03:08data set
03:10and finally
03:12you have to do
03:14actual test data.
03:18The model comparison
03:20is called test data
03:22and the tuning model
03:24is called validation data
03:26which basically
03:28train
03:30is called train data.
03:32Make sense?
03:34those three sets
03:36are.
03:38You have to choose
03:40and train
03:42and train
03:44you have to do it.
03:46You have to do it.
03:48You have to do it.
03:50You have to do it.
03:52You have to do it.
03:54You have to do it.
03:56Basically
03:58and finally
04:00I have to do it.
04:02I have to do it.
04:04You have to do it.
04:06You have to do it.
04:08You have to do it.
04:10So if you have to do it,
04:12you have to do it.
04:14If you have a test data
04:16you have to train
04:18if you have to do it.
04:20You can't show it.
04:22So this is why this is important for the accuracy of the model.
04:33This is why this is important for the three data sets, whether it is training or validation or model,
04:41it will be separate to the model.
04:46The example I have given is that in machine learning,
04:56if you have the test data to train,
05:01the accuracy is 100%, marks are 100 by 100,
05:06you can represent the true capability, not.
05:11This is called overfitting.
05:13In machine learning, this is called overfitting.
05:18On the other hand, if you have the test data,
05:21if you have the test data,
05:23this is called overfitting.
05:25This is the terminology,
05:27which is called underfitting,
05:31it is called underfitting.
05:33Okay?
05:34Underfitting.
05:36Okay?
05:37This is the two things you have seen.
05:40Basically,
05:41in machine learning terms,
05:44if you have the test data to train,
05:50the accuracy is 100%,
05:52marks are 100 by 100,
05:54but the markers are the true capability,
05:57you can represent the true capability.
05:59It is not.
06:00It is called overfitting.
06:03In machine learning terms,
06:05it is called overfitting.
06:08On the other hand,
06:09you can't do it.
06:10The test data is called overfitting.
06:12You can't do it.
06:13This terminology,
06:15which is the accuracy,
06:18the accuracy is low.
06:20It is called underfitting.
06:22Underfitting.
06:23Okay?
06:24Underfitting.
06:25Underfitting.
06:26Okay?
06:27Okay.
06:28These two things you have seen.
06:29Basically,
06:30there are three basic components.
06:33I repeat and then,
06:34I repeat and then,
06:35then,
06:36then,
06:37you can choose and train.
06:39But,
06:40when I talk about it,
06:42when I talk about it,
06:43in the background,
06:44on the context of it,
06:45there is data.
06:46There is data.
06:47You can train,
06:48tune,
06:49and compare it.
06:50You can compare it.
06:51What is the model?
06:52You can compare it.
06:53I am sure.
06:54I don't know,
06:55so.
06:56In the case of,
06:57this data,
06:58I know,
06:59the data,
07:00the data,
07:01which we have used,
07:02the data.
07:03As we say,
07:05the data,
07:06is the data.
07:07It's got a trained data.
07:09Which we have used,
07:11Now,
07:12which we have used,
07:13which we have used do,
07:14which we do.
07:15The model true.
07:16We have used to know
07:17how to change these items.
07:18it is like this example, if you have a practice exam, practice exam is so important,
07:23that you can do it, you can train, that you can validate it,
07:28then the exam on the validation, which we call the machine learning, validation data.
07:36And the final data, which you have final test, which you call test data,
07:42and then you compare the model.
07:45Now, the model tuning is very important.
07:49In comparing, we will go to the model tuning which is very important.
07:53So, I will do this in this way.
07:56So, the model tuning is on the model tuning.
08:00You can see that the model accuracy is not good.
08:04So, in every model, whether you go to the regression model,
08:10or in the neural nets, or in the random forest,
08:16there is one thing that exists.
08:20It is called hyperparameter.
08:26Hyperparameter.
08:28Hyperparameter, you can understand that
08:31it is a small button that you can change your car.
08:38You can imagine that in the cockpit,
08:41there are many buttons that you can change your car.
08:44Then, you can change your car.
08:47In the machine learning,
08:50there are hyperparameter.
08:52So, you can play the hyperparameter.
08:54So, you can play the model of fine tuning,
08:58or the result,
08:59the required result will be achieved.
09:02So, for example,
09:04you can play the other part.
09:06Like this, you can play the DJ.
09:08You can play the DJ,
09:09or you can play the music,
09:10or you can play the music.
09:11It will be fine tune for experience.
09:13Okay.
09:14Hyperparameter, you can also have idea.
09:16Excellent.
09:17So, you can do it here.
09:19Let's close.
09:20Now, we have only one thing left.
09:22Validation, we have also learned about the data splitting.
09:25We have learned about the data splitting.
09:273-4 concepts
09:29basically
09:31model validation
09:33next lecture
09:35we will see
09:37modeling
09:39basic parts
09:41split
09:433 parts
09:453 different data types
09:47training validation
09:49test
09:51tuning
09:53tuning
09:55hyper parameter change
09:57training
09:58basically
09:59we have seen
10:00model
10:01train
10:02this
10:04model
10:05accuracy
10:06and
10:07compromise
10:08time
10:10accuracy
10:11model
10:1290%
10:13accuracy
10:14computer
10:15run
10:16so
10:1790%
10:18customer
10:19run
10:20so
10:21you
10:22train
10:23accuracy
10:24so
10:25this
10:26training
10:27time
10:28versus
10:29computer
10:30accuracy
10:31a
10:32c
10:33versus
10:34a
10:35c
10:36u r a c y
10:37accuracy
10:38so
10:39these
10:40two
10:41things
10:42that
10:43time
10:44versus
10:45accuracy
10:46time
10:47accuracy
10:48we can
10:49discuss
10:50we can
10:52discuss
10:53this
10:54lecture
10:55close
10:56next
10:57lecture
10:58model
10:59comparison
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